Reviewerly/exHarmony huggingface dataset:
Evaluation User Acceptance of Mobile Virtual Reality Application for English Learning

This research develops virtual reality that integrates media elements such as sound, animation, and video with virtual world to create an immersive user experience. It is aimed to provide more interactivities and interesting features in supporting learning activities, such as using Cardboard to observe the virtual world and interact with virtual objects. Previously, a prototype was built to evaluate (high school) students’ interests in using virtual reality technology as a supporting media to learn English language subject at high schools in Yogyakarta. Earlier research shows that students agreed to use of virtual reality prototype had been useful to support their learning activities. In this present research, a mobile application prototype is developed and evaluated using TAM methodology in order to understand the user behavior towards the application. The application of TAM in the field of learning and teaching for various learning domains implemented to understand the acceptance from the users while using Virtual Reality-based learning media. The TAM model It is a model can explain that the user’s point of view will determine their attitude in accepting Virtual Reality-based learning applications. The evaluation results from this study showed that respondent is enjoyable to use this application (4.37 mean point) and 3.98 (mean point) feels that Virtual reality application is useful for learning English.
Measurement of Employee Information Security Awareness on Data Security: A Case Study at XYZ Polytechnic

Information security is a critical national policy issue. Cyber-attacks and information security breaches are becoming more and more common. Fears of a growing attack could occur far outnumber the recorded cases. This is felt at the XYZ Polytechnic, there were 926 cases of Brute Password Attacks in the third quarter of 2021. Efforts for information security have not been fully carried out. Therefore, it is necessary to know the level of information security awareness, especially among XYZ Polytechnic employees, and develop strategies to improve information security. The measurement uses HAIS-Q with seven areas of information security. An information security assessment is processed using AHP method. This study pointed out that the value of focus area was at the medium level of consciousness (66.5%). Based on the results obtained, diverse strategies in terms of technology and human resources are required to supervise and raise the level of information security awareness at XYZ Polytechnic.
Information Technology Governance at Rachmi Dewi Gresik Hospital Using the Cobit 5 Framework

Earlier nineteenth century, in Europe and the United States, management information systems have been developed in hospital and clinic inorder to support the health service during decades until now. As a result, it brings complexity and rigor that leaves patients feeling dissatisfied, which requires the adoption of IT governance around the world. After several years, it turns out that there are several problems faced specifically by Rachmi Dewi Gresik Hospital. Thus, several aspects need to be improved to achieve the vision mission lead to primary objectives. Therefore, this study aims to assist Rachmi Dewi Gresik Hospital in making improvements from the point of view IT governance using the COBIT 5 framework so that the strategic planning by the organization can be aligned. COBIT 5 is used to develop documentation and guidelines based on good governance principles. It is widely recognized as a framework capable of supporting auditors, management, and users to bridge the gap between business risks, control requirements and issues with technical issues.
Compressed YOLOv5 for Oriented Object Detection with Integrated Network Slimming and Knowledge Distillation

In recent years, object detection has been expanded to drone scenes, where remote sensing images contain a greater variety and arbitrary-oriented targets. In order to solve the problem of detection difficulty and computational intensity for remote sensing images, oriented object detection is needed and the network model is expected to be deployed on resource-limited devices. This paper proposes a lightweight object detection method for oriented object detection by leveraging and compressing YOLOv5 network model. We integrate the fine-tuning stage in network slimming with knowledge distillation to enhance the accuracy of the detection model and save training time by transferring the important feature information to the student network. Loss function is redesigned by combining Theta loss with other detection and distillation losses to make the compression model more accurate. Extensive experiments are conducted to verify the effectiveness of our proposed method on the remote sensing public dataset DOTA. The compressed model achieves an accuracy of 76.18% on the DOTA dataset, 1.7% increase compared to the original YOLOv5 model. The FLOPs are decreased by 37.0%, the number of parameters is decreased by 58.9%, the weight file size is decreased by 57.6%, and the inference time is decreased by 17.4%.
YOLOv5-GE Vehicle Detection Algorithm Integrating Global Attention Mechanism

Vehicle detection is an important technology in au-tonomous driving, for which high detection accuracy and real-time performance are often required. The YOLOv5-GE vehicle detection algorithm is proposed to address the situation that the YOLOv5 vehicle detection model has false detection and missed detection for small and dense targets in complex environments. The global attention mechanism is added to the backbone net-work of the YOLOOv5 model, which is composed of two inde-pendent submodules of channel attention and convolutional spa-tial attention, which prevents the loss of information to a certain extent and amplifies the interaction of global dimensions. Second-ly, the training process is optimized using the Focal-EloU loss function to replace the GloU loss function, which improves the accuracy of vehicle detection. Finally, the proposed YOLOv5-GE algorithm and the YOLOv5 algorithm are subjected to a con-trolled experiment on the KITTI dataset. The experimental re-sults show that the YOLOv5-GE algorithm achieves an average accuracy of 86% while maintaining real-time performance, which is 2.5% higher than that of the YOLOv5 algorithm, and can im-prove the detection accuracy of small and dense targets in com-plex environments.
Differential Privacy Protection Algorithm Based on Zero Trust Architecture for Industrial Internet

The Zero Trust Architecture is an important part of the industrial Internet security protection standard. When analyzing industrial data for enterprise-level or industry-level applications, differential privacy (DP) is an important technology for protecting user privacy. However, the centralized and local DP used widely nowadays are only applicable to the networks with fixed trust relationship and cannot cope with the dynamic security boundaries in Zero Trust Architecture. In this paper, we design a differential privacy scheme that can be applied to Zero Trust Architecture. It has a consistent privacy representation and the same noise mechanism in centralized and local DP scenarios, and can balance the strength of privacy protection and the flexibility of privacy mechanisms. We verify the algorithm in the experiment, that using maximum expectation estimation method it is able to obtain equal or even better result of the utility with the same level of security as traditional methods.
Age Estimation with Synthetic Mask Generation Based on MobileNet and Facial Keypoint Detection

Facial age estimation is one of the most important tasks in the field of face recognition and recommendation system. Since the COVID-19 pandemic, people have been required to wear masks, which can be a challenge for traditional recognition methods. In this paper, an improved convolutional neural network architecture based on MobileNet is proposed to perform age estimation. For the challenge of masked faces, an innovative mask generation method using face keypoint detection is adopted, extracting the key points of the faces in order to add synthetic masks to simulate the real situations. Then we compare the estimation results of the original images and the synthetic images. Our method is applied to the WIKI Face dataset containing more than 150,000 images, and achieves MAE of 3.79 and 6.54 on unmasked and masked faces, respectively, which demonstrates the effectiveness of the proposed model.
Representation based Few-Shot Learning for Brand-logo Detection

In this paper, we propose an attention-net based few-shot object detection (AN-FSOD) model for brand-logo detection and recognition. With the fact that brand-logo detection has many distinct properties: tiny objects, similar brands, and adversarial images, most of the current FSOD approaches, motivated by meta-learning, metric-learning and transfer learning techniques, typically perform less-effective due to the difficulties on target region allocation. The proposed AN-FSOD aims to locate the region of the brand-logo targets, achieved by a well- trained attention-net, therefore providing an explicit feature maps for detection and classification. An end-to-end feature extractor and target detector model is established, implementing with a simultaneous parameter fine-tuning with respect to the few-shot dataset. Extensive simulations have confirmed that the proposed AN-FSOD gains significantly better performance than the vanilla FSOD model and the majority of the feature extractor aligned model on a public brand-logo dataset.
Hierarchical Path Planning based on PPO for UVs on 3D Off-Road Terrain

In 3D off-road terrain, the driving of the unmanned vehicle (UV) is influenced by the combined effect of terrain and obstacles, leading to greater challenges in safety and stability. Many path planning studies on 3D off-road terrain are lacking in the consideration of this combined effect, for which we implemented a continuous simulation system and made the following contributions. First, an integrated potential field that is adaptive to the size of obstacles is proposed, and then it is used as a movement cost in the Voronoi diagram (VD) to obtain a pre-planned path for obstacle avoidance. Second, the Proximal Policy Optimization (PPO) algorithm is used to reduce the task objective, the effects of terrain and obstacles, and vehicle motion constraints to an objective function to be optimized. Finally, to improve the learning efficiency and generalization ability, we propose a hierarchical path planning (HPP) method to introduce the derived pre-planned path into the learning process of the PPO algorithm. It is experimentally demonstrated that the PPO algorithm combined with the HPP method is able to accomplish the path planning task in 3D off-road terrain of different sizes and difficulties, and obtains higher accuracy and shorter 3D path than the shaping reward (SR) method. The final path is suitable for the UV without smoothing, and the UV automatically adjusts its steering on undulating terrain to improve stability, and automatically slows down to pass through sharp turns to reduce the risk of rollover.
Home intelligent assistance mobile robot for the elderly

With the continuous development of human economy and society, the continuous improvement of material civilization, and the continuous improvement of people’s living standards, the quality requirements for living households are also constantly improving. The use of machines to replace people to complete tasks such as cleaning and sanitation in the home, and to release people from the complicated housework has become the life concept of more and more people nowadays. At present, most of the home service robots are mainly disabled robots and housework robots such as sweeping robots. The functions are relatively simple. A family often needs to be equipped with multiple robots to meet people’s needs, and the cost is high. In addition, there are still many manual labors in the family that need to be done by people, such as carrying heavy objects, knocking nails, screwing screws, etc. Now that all work is becoming more intelligent and automated, people are eager to do these tasks by machines. In 2019, the “Joint Tips for Preventing Falls for the Elderly” jointly issued by seven institutions including the Chinese Society of Gerontology and Geriatrics, the Chronic Disease Center of the Chinese Center for Disease Control and Prevention, and the Beijing Institute of Geriatrics of the National Health Commission showed that falls are the most common cause of falls in my country. The number one cause of injury death among people over 65 years of age. Falls are a serious threat to the health of the elderly. The main cause of traumatic fractures in the elderly is falls. More than half of the elderly who visit medical institutions due to injuries each year are caused by falls. At the same time, the older the elderly, the higher the risk of injury or death from falls. Based on the above background, we designed this multi-purpose household auxiliary mobile robot. This robot has three forms, and is equipped with a handling robot arm and a blessing robot. Through deformation, the mechanical leg straightens and enters the standing state, which can help middle-aged and young people in the family to complete some physical tasks such as carrying heavy objects; when the mechanical leg is folded, the machine can be used as a movable chair to assist the elderly to move, and one-time handling More items; when the device is transformed into a car shape, it can help family members to move quickly.
An optimization ant colony algorithm based on fixed point theory

the convergence accuracy and stability of ant colony optimization algorithm (ACO) are in urgent need of improvement ever since it has been proposed. Existing research attempt to optimize the algorithm from various perspectives, but most of them are relatively ex prate or application specific. In this paper, we design an elite strategy to optimize the initial parameters and build a Fixed-point ACO improved algorithm. The process is that it first converts the problem of targeted function optimization to a problem of fixed-point searching. Then the solution of the equation set is obtained by simplicial algorithm (SA) of fixed-point theory. Finally, the solution set will be regarded as the initial population of ACO algorithm and the remaining parameters are set accordingly. The experimental study is carried with five testing functions from UCI database. The results indicate that FP-ACO algorithm is significantly better than the conventional algorithm both on the average speed of convergence and the average accuracy of global optimum. Besides, the searching process of FP-ACO algorithm has a better stability by presenting a state of continuous optimization.
Light Enhancement Algorithm Optimization for Autonomous Driving Vision in Night Scenes based on YOLACT++

In scenes with low lighting at night, the outline of objects that need to be recognized, such as vehicles, is not clear, and cannot be accurately recognized by the automatic driving system. At present, there are many researches on instance segmentation models, but there are few researches on the instance segmentation application of automatic driving night scenes. According to BDD100K dataset, the automatic driving daytime scene dataset is marked. First, we perform data augmentation by using gamma correction to simulate the night driving scene in the training phase. Then we use our improved low-light enhancement algorithm with gradient increment based on RetinexNet in the prediction phase to brighten night driving scene images. Furthermore, we evaluated our proposed method on YOLACT++ model. The results show that the improved YOLACT++ automatic driving night segmentation ability has been significantly improved, the segmentation of vehicles at night is more accurate and robust, and it has better application value in night automatic driving scenarios.
An End-To-End Visual Odometry based on Self-Attention Mechanism

To address the problem of capturing and expressing key features in existing methods, we design an end-to-end visual odometry algorithm using a self-attention mechanism. The algorithm consists of two parts: Visual Transformer network structure and Bidirectional Attention Long Short-Term Memory network. The former can extract visual features from video or image sequences, and the latter can mine the correlation between images captured on long trajectories. The algorithm can enhance the localization accuracy and robustness of visual odometry. The extensive experiments based on the KITTI benchmark demonstrate that the proposed algorithm works better than other outstanding algorithms.
Application of Profiling Technology in Enterprise Financial Data Analysis

Invoices can extract a large amount of information and use data to apply value to build a supplier’s profile. By searching a certain procurement report, the basic information such as the supplier’s name, supplier list identification number, address industry, and related companies and historical issuance are displayed layer by layer, and the full picture of the supplier can be clearly displayed through a report of the supplier. Analyzing suppliers can realize risk warning, improve decision-making suggestions for managers, and improve the efficiency of precise policy implementation.
Construction of a Decision Model for the Evaluation System of Practical Teaching Quality Based on AHP

This paper constructs a decision model for the evaluation system of practical teaching quality, which is composed of five first-level evaluation indexes such as practical teaching objective, practical teaching design, practical teaching resources, practical teaching implementation and practical teaching effect, and 17 second-level indexes such as explicit teaching objective. The evaluation system has the characteristics of strong operation and comprehensive coverage of indexes, which can effectively promote the quality of practical teaching.
Straight From the Human Factors Professionals’ Mouth: The Need to Teach Human Factors in Cybersecurity

Colleges and universities are vital for integrating the human factors discipline in cybersecurity courses. Human errors, limitations, and weaknesses contribute to data breaches, ransomware attacks, and cyber-attacks. The cybersecurity community struggles to leverage human factors as a scientific discipline to eradicate human-related issues. Problems regarding the human element in cybersecurity require curricula focusing on the scientific aspects of human factors to teach human factors principles. As people and technology are increasingly interdependent, students interested in pursuing careers in technology must have a foundational understanding of people and their interactions with technology. While human factors is the discipline, human factors engineering is the work of leveraging human factors principles to improve the integration between humans and systems. Through various venues, educational initiatives, and research, academia can systematically link the science of human factors with cybersecurity. Colleges and universities are a centric node to educate industry, academia, and government leaders on the value of human factors engineering in cybersecurity. Through scholarly research, partnerships with government and industry, and a developing human factors curriculum, academia can influence business decision-makers to leverage human factors engineering with the same rigor and affinity for cybersecurity, software, and network engineering. This panel serves as a platform to increase awareness and the significance of integrating human factors courses into cybersecurity curricula that are taught by faculty members with proper credentials and industry experience.
Introducing Zero Trust in a Cybersecurity Course

Zero trust (ZT) is a conceptual and architectural model for cybersecurity teams to design networks into secure micro-perimeters and strengthen data security by systematically integrating state-of-the-art technology, risk management, and threat intelligence. ZT has recently gained momentum in the industry to defend against lateral movement of malicious actors in today's borderless networks. The United States 2021 President Executive Order requires the federal government must adopt security best practice and advance toward a zero trust architecture (ZTA). However, it is not a trivial task to implement a ZTA due to its novelty and complexity. We need to understand what ZTA is to take the advantage of it. Therefore, there is a need to introduce the fundamental concepts, principles, and architectures of ZT in cybersecurity courses at a college to better prepare our new cybersecurity professionals for their careers.
Improving matrix-vector multiplication via lossless grammar-compressed matrices

As nowadays Machine Learning (ML) techniques are generating huge data collections, the problem of how to efficiently engineer their storage and operations is becoming of paramount importance. In this article we propose a new lossless compression scheme for real-valued matrices which achieves efficient performance in terms of compression ratio and time for linear-algebra operations. Experiments show that, as a compressor, our tool is clearly superior to gzip and it is usually within 20% of xz in terms of compression ratio. In addition, our compressed format supports matrix-vector multiplications in time and space proportional to the size of the compressed representation, unlike gzip and xz that require the full decompression of the compressed matrix. To our knowledge our lossless compressor is the first one achieving time and space complexities which match the theoretical limit expressed by the k -th order statistical entropy of the input. To achieve further time/space reductions, we propose column-reordering algorithms hinging on a novel column-similarity score. Our experiments on various data sets of ML matrices show that our column reordering can yield a further reduction of up to 16% in the peak memory usage during matrix-vector multiplication. Finally, we compare our proposal against the state-of-the-art Compressed Linear Algebra (CLA) approach showing that ours runs always at least twice faster (in a multi-thread setting), and achieves better compressed space occupancy and peak memory usage. This experimentally confirms the provably effective theoretical bounds we show for our compressed-matrix approach.