Reviewerly/exHarmony huggingface dataset:
Approach for the construction of gestural interfaces to control graphical interfaces based on artificial intelligence

Due to hygienic concerns, the COVID-19 has accelerated the use of touchless technology, which has expedited the transition to Zero User Interface. This Interface (UI) refers to a controlled user interface that enables interaction with technology using gestures, voice, eye tracking and biometrics like contactless fingerprints and facial recognition. These control interfaces incorporate interaction techniques like speech and gestures. The advancement of touchless interaction with hand gesture interfaces is the main topic of this study, these interfaces are specialized programs that track and predict hand gestures to give alternative controls and interaction techniques. The hand gesture interface consisting of four main layers: Hand Gesture Interface, Mapping Gesture, Action, The Input Simulator, and Graphical User Interface. In addition to our interface, we employed the new algorithm for according to the hand-type classification. By doing trials on a gastronomy application, we confirm our methodology. With five volunteers, we conducted a small-scale user study to evaluate and test the hand gesture interface. User feedback indicates that the hand gesture interface is simple to be used.
Handwritten Arabic characters recognition using Capsule Networks

Handwritten text exhibits a diversity in styles, and unpredictable characteristics making of Handwriting Recognition (HWR) an interesting computer vision problem. In the case of Arabic script, the recognition task is especially more complex, due to its cursive nature, and emphasis on fluid and connected pen strokes, rarely seen in other scripts. Unfortunately current end-to-end approaches fail at modeling the structural aspect of visual information with high accuracy, which is detrimental for tasks such as Optical Character Recognition (OCR). Capsule networks are a neural network architecture, using a set of interesting concepts which can accurately model the structural aspect of image features, to solve some of the Arabic script recognition problems faced by other systems. Our proposed approach based on Capsules yielded interesting results using a data set of around 50 thousand Arabic characters with neither binarization nor noise reduction, while achieving more than 97% TOP-1 accuracy.
A Blockchain-Based Patient Electronic Health Record System – Etmaen (اِطْمَئِن)

In this paper, we propose a blockchain patient electronic health record system called Etmean (اِطْمَئِن) to handle medical records sharing and transfer in healthcare centers. Our main goals are to help ease the challenges faced in the healthcare sector: multiple records for one patient, difficulty prioritizing emergency rooms (ER) cases, and lack of medical records security. Blockchain is a promising, trusted technology that has a digital ledger of transactions supported by a network of computers in a form that makes it hard to hack or modify. Our implemented system fulfills privacy, security, and accessibility requirements in a healthcare sector such as hospitals and clinics.
Few-shot Classification based on CBAM and prototype network

In recent years, with the continuous progress and development of deep learning, computer vision problems have been solved to a large extent, but the problem of few-shot has also appeared. Deep learning requires a lot of training data, so there is still a lack of learning from few samples. This paper focuses on the image classification problem in the few-shot problem as the research object. Firstly, based on the residual network ResNet18, and adding the convolution attention mechanism on this basis, the feature extraction network Res-CBAMnet is constructed. Secondly, the prototype network is used as the classifier to study the influence of different metric methods on the classification results. Experimental results show that the improved network achieves 99.72% and 67.42% accuracy on the few-shot image classification benchmark datasets Omniglot and mini-imagenet respectively.
A Federated Learning Approach for Water Distribution Networks Monitoring

Nowadays, people's demand for water is growing as well as in public, commercial, and industrial sectors. However, the limited character for water resources presents a crucial obstacle to satisfying needs for continued human development. The control and provision of potable water are therefore the most vital challenges for the water supply system. It must use water resources in an efficient manner, and fulfill both quality and quantity demands. For this purpose, the current water supply system uses smart infrastructure that gathers, processes, stores, and delivers water from water sources to users. It is done in a very complex environment with ever-increasing demand, and often conflicting services to deliver. In this work, we developed a Federated Learning (FL) model, which is come in the form of a machine learning setting where clients collaboratively train under the orchestration of a central server while keeping the training data decentralized. The FL embodies the principles of focused data collection and minimization, it can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Implementing on the central server, the FL will iterate on the gateway's learning models using the federated averaging algorithm. Our approach's capacity is shown by addressing reel instances in a real-world issue in which the proposed method finds much better results and a significant improvement in performance compared to the standard approaches while achieving our goal and suggesting a new interesting direction for research.
Port staff trajectory exploitation via an emsemble detection-tracking framework

For the safety supervision of port environment, we propose a port staff detection and tracking framework. Firstly, the framework is based on Faster-RCNN (Faster Region Convolutional Neural Networks) detection algorithm to identify the port staff in the surveillance video, and obtain the target location information. Based on the Deep SORT (Deep Simple Online and Realtime Tracking) tracking algorithm, we add Gaussian noise reduction and histogram equalization to preprocess the input image, and then use Kalman filter and Hungarian algorithm to predict and match, so as to improve the accuracy of target tracking. The framework effectively improves the accuracy of the algorithm, provides a powerful technical support for port security monitoring, and provides a security guarantee for port staff.
New Triggers for Automatic Camera Placement Over Time

We propose new trigger variants for in situ automatic camera placement over time (ACPOT). We evaluate the performance of these variants on three data sets from two simulation codes. We find that our approach has two major benefits: (1) it mitigates a problem where the camera “stagnates” on a view occluded from interesting phenomena and (2) proposes alternate trigger criteria that provides comparable camera placement quality (evaluated using a entropy-based viewpoint quality metric) with reduced computational cost.
Siamese Multi-Scale Aggregation Network for UAV Tracking

The Siamese-based trackers have received much attention due to their great performance in the field of target tracking. However, it ignores the relationships and interdependencies between different features, impeding the robustness under various conditions. In addition, most Siamese-based trackers suffer from multiple special challenges, such as Fast Motion, Occlusion in UAV tracking. In this paper, we propose an anchor-free based object tracking algorithm with multi-scale aggregation Siamese Network. The proposed method consists of three parts: the feature extraction network, Encoder and Decoder. A multi-scale receptive field structure is designed in the encoder to deal with the problem of multi-scale change. The design of adaptive anchor in the decoder effectively reduces the relevant hyper-parameters. Experiments on three challenging UAV tracking benchmarks have demonstrated the robustness and effectiveness of the proposed method.
EFEKTIVITAS TUTON TERHADAP KEMANDIRIAN DAN CAPAIAN HASIL BELAJAR MAHASISWA FKIP UNIVERSITAS DARUSSALAM AMBON PADA MATA KULIAH PENGANTAR APLIKASI KOMPUTER

Constraints encountered during online practicum in the Introduction to Computer Applications Course, namely, an unstable network that causes communication gaps between students and lecturers during practicum, limited facilities (laptops) owned by students, inadequate internet quota, student learning abilities and independence different ways of operating the software. The purpose of this study was to determine the effectiveness of online tutorials on independence and achievement of student learning outcomes of FKIP UNIDAR introductory computer application courses. This research is a pre-experimental design research type One-Shot Case Study with the subjects of FKIP students of Chemistry Education, Physics Education, Mathematics Education and Biology Education courses taking introductory courses on computer applications for the 2021/2022 even academic year. Data on the validity of online tutorials and student learning independence were obtained through the distribution of questionnaires, while the cognitive aspects were obtained through post-test. The results obtained indicate that online tutorials are effectively used as a practical medium in online learning, with the acquisition of student learning outcomes 78.57% getting good grades and student learning independence after learning through online tutorials is in the good category.
Blockchain Technology in Supply Chain Management: Challenge and Future Perspectives

Modern supply chains include multi-layered and geographically separated entities, which leads to the globalization of supply chains and the need for separate regulatory policies. Because of that, the process of managing and controlling information and material flows throughout the supply chain structure has become laborious. Nevertheless, being in the era of information and communication technology, various technologies have been proposed for the facilitation of more efficient supply chain management. One of the cases that have been widely discussed in recent years is the use of blockchain technology as a distributed digital ledger that guarantees the integrity, traceability, transparency, and security of information. In this study, we discuss the overall potential for Blockchain in supply chain management to shed light on this burgeoning subject.
WMC-ViT: Waste Multi-class Classification Using a Modified Vision Transformer

The constant production and lack of efficient waste management procedure has created a need for automated classification of trash as it comes into facilities. This paper proposes a new algorithm for efficiently classifying objects found in solid waste processing by utilizing a combination of vision transformers (ViT) and convolutional neural networks (CNNs) to create a Multi-Head block for parallel processing of multiple transformers. This method identifies five unique classes of the most common material found in waste with peak test accuracy of 94.27% using 35492 total parameters, a reduction of 99.74% when compared to current state of the art methods, allowing for lower power operations and easier deployment.
Classifying the Devil in the Dust: Edge AI

One of the most challenging and significant objectives of NASA over the coming years is to successfully send humans to Mars. The past six decades of safety concerns addressed for satellite and rover missions, become an even more important consideration with human passengers. Atmospheric and surface conditions of Mars can change abruptly, leading to communication breaks, equipment failures, and potential safety threats. The sudden onset of a dust storm, or a more common dust devil, can interfere with atmospheric entry, ground mechanical equipment, solar charging systems, and much more. By combining traditional signal processing techniques and with an efficient machine learning algorithm, this paper proposes to classify atmospheric disturbances on the red planet with a high level of accuracy.
Blockchain Technology and Its Role in Industry 4.0

Industry 4.0 is the next phase in the transition of traditional factories into smart factories that are resource efficient and exceptionally adaptable to changing customer requirements. It is worth noting that the Industry 4.0 paradigm encourages the use of such technologies to enable the evolution of factory communications architectures away from cloud- or Internet-service-centric architectures and towards architectures in which all entities involved in industrial processes share information via a peer-to-peer (P2P) network.
User interface modeling technique in the cloud environment

For designing a qualified user interface, there needs to be a graphic expert, requirement analyst, system designer, programmer, technical expertise, social activity scientist, and experts for each field. However, it is extremely difficult for these various experts to participate in such user interface design. This paper focused on design rules and modeling techniques of user interfaces that can support user availability. The visual cohesion of business events can be improved by modeling the prototype of the object-oriented user interface based on the object. The clustering method uses transaction objects and forms objects based on business event objects and task objects. We have studied it in detail so that a prototype of the user interface can be created. The significance and conclusions of this study are as follows. First, visual cohesion is improved by designing the object of functional, sequential, and communicative objects. Second, the object design rules of the user interface were created so that even an inexperienced designer could create a high-quality prototype. Third, it enhances the user's preference, ease, understanding, compliance rate, and quality of graphic layout by improving object-based visual cohesion. Fourth, functional, sequential, communicative, and procedural cohesion of business events is increased by the clustering of user interface objects. As a result, this paper is providing a modeling method of user interface technique in the cloud environment that could enhance the visual cohesion of user interface prototypes.
How Can Blockchain Benefit Payment Systems in Smart Construction Contracts: A Brief Review

Abstract Construction industry professionals suffer regularly from poor cash-flow which reflects non-payment or payment delays down the hierarchical chain. This issue is important as the construction industry has the highest rates of insolvency in Australia, the UK and many other countries. Payment conditions under current construction contracts have proven to be inefficient in delivering timely payments as human interference has control over processing claims. This paper investigated the status of contracts and contract law in Australia and the potential of smart contract technology in improving payment issues in the industry. Qualitative data was collected from secondary literature sources which included observations from industry professionals, real case studies, secondary research and government surveys. It was found that smart contracts feature self-executing digital contracts, immutable data, require no intermediaries and provide transparency on all levels. Although these features are fit for purpose in resolving current contractual issues, smart contracts are not yet available in the construction industry. It was also found that smart contracts do have the potential to provide a trusted and reliable payment system in the construction industry, although there are some aspects it is unlikely to replace such as human performance. Research limitations and future research directions are also provided.
USING THE DIDACTIC CAPABILITIES OF INTERACTIVE EDUCATIONAL RESOURCES TO IMPROVE THE PRIMARY EDUCATION SYSTEM

The effectiveness of the digital learning process is determined by the educational resources used. The educational environment, which is implemented in the process of digital learning, and the unity of this process form an ecosystem of digital learning. The article discusses the issues of use of interactive educational resources in improving primary education system.
CUPT Physical Innovation Talent Cultivation Under the CDIO Model

With the enactment and implementation of the National Medium and Long-term Education Reform and Development Plan (2010-2020) in March 2010, the requirements of quality education is the strategic theme of education reform and development and insisting on ability as the priority and other requirements have put forward clear requirements for the cultivation of talents in our universities. [1] On this basis, researchers from some universities in China specializing in physics have carefully studied the concept and various aspects of the IYPT (International Young Physicists' Tournament), fully realizing that this form of competition is different from the traditional scientific research training in our universities and has positive significance in improving the creative awareness, innovation, collaboration and practical skills of university students specializing in physics.
Quantum Guess and Determine Attack on Stream Ciphers

Abstract To guarantee the security of symmetric key schemes against quantum adversary, developing quantum cryptanalytic techniques becomes a major worldwide challenge in the post-quantum world. In this paper, we present a general framework of classical guess and determine attack on stream ciphers, and then convert it into quantum guess and determine attack. It shows that, for a given stream cipher with a key size of $k$ bits and an internal state size of $n$ bits, if a basic guess and determine attack with a time complexity below $O ( {{{2}^{{3k}/{2}}}}/{n} )$ is available, there is a quantum guess and determine attack with multiple data that can recover all $n$ internal state bits of the cipher with complexity below $O ( {{2^{k / 2}}} )$. As applications, we present quantum guess and determine attacks on the SNOW-like stream ciphers. The results show that all of SNOW 1.0 with 128-bit key, SNOW 2.0 with 128-bit key and SOSEMANUK are insecure against quantum guess and determine attack. The resource requirements for implementing a quantum guess and determine attack on SNOW 3G are evaluated as a case study. To the best of our knowledge, this is the first time that the general quantum guess and determine attack is formally proposed and applied to the SNOW-like stream ciphers.
A Survey on Optical Adversarial Examples Against DNNs

Adversarial attack always affects the security of artificial intelligence. In recent years, a new type of non-invasive adversarial attack becomes very popular among the studies for its flexibility and excubility, and this paper has generated great interest in it. In this paper, four types of this kind of adversarial attack are analyzed and discussed in detail: camera high-frequency light, laser, projector and natural light shadow.
Application of Image Recognition for On-board Sailor Behavior Based on Broad Learning System

Based on broad learning system (BLS), this paper monitors and identifies the behavior of crew members on ships. The main recognition scenarios include both of on deck and in the cabin, and the main recognition tasks include crew tracking and identification. In this paper, the video data is divided into frames and images are segmented. In the preprocessing of images, the filter is used to enhance the information and reduce the noise of the images. Finally, the recognition model is established by the BLS. In the experiment, we use BP neural network (BPNN) and support vector machine (SVM) as comparisons. The proposed method in this paper has achieved the best results in terms of recognition accuracy and training time.
Evolutionary Neural Architecture Search Based on Variational Inference Bayesian Convolutional Neural Network

In past decades, Bayesian neural networks have attracted much attention due to their advantages of being less prone to over-fitting and being able to generate uncertain measurements with discriminant results. However, Compared with traditional neural networks, Bayesian neural network has too many hyper-parameters to be optimized, so that its performance in classification or regression problems on large-scale datasets is not much superior to ordinary neural networks. Therefore, in order to design a Bayesian network with superior performance, we propose VIBCNN-EvoNAS, a Bayesian convolutional neural network architecture search framework based on variational inference, which constructs a search space through a fixed length integer encoding scheme, and uses evolutionary algorithm as a search strategy to deeply explore the influence of convolution kernel size and other related parameters on the network architecture. In addition, in order to reduce the time loss caused by individual evaluation, we adopt the early stop mechanism in the performance evaluation stage. The proposed method is evaluated on CIFAR10 and CIFAR100 datasets, and the experimental results show the effectiveness of the proposed method.
Multi-scale Enhanced Fine-grained Feature-based Person Re-identification Algorithm

The key to solve the problem of Person Re-identification is to improve the extraction and application of Person effective features. Convolutional neural networks have powerful capabilities in this regard. This paper proposes a Person re-recognition algorithm based on multi-scale enhanced fine-grained features. Resnet50 is used as the backbone network to extract Person features at different scales, and the EFOM module is proposed to enable the extraction of fine-grained features by adding relevant global features while compensating for the shortcomings of its own attention mechanism to obtain enhancement and refinement. Finally, the MFFP module is used to obtain the fused features at different scales and then stitched into the BNNeck module. The fused feature vectors are supervised and trained using a variant triplet loss function with less overhead and a more flexible central loss function. Experimental results of the method on the DukeMTMC-ReID and Market-1501 datasets show that it achieves 86.7%% and 92.0% on the mAP evaluation metric; 91.1% and 94.8% on the Rank-1 evaluation metric. The experimental results show that the method makes full use of different scale feature information and key fine-grained features. It enhances the recognition degree of person features and improves the efficiency of person Re-ID.
Development of A Robust Privacy Enhancing Data-Sharing Framework for Healthcare System Based on Decentralized Control

Blockchain technology enables decentralized, immutable, verifiable digital asset transaction records. Any interested party can access these documents. Affected industries include healthcare. The use of Electronic health records raises security concerns. Blockchain technology could secure this sensitive data. Transaction relatability, crypto-key control, and on-chain data privacy are security and scalability challenges for blockchains. Blockchains confront scalability and security issues (e.g., adhering to privacy legislation). Many researchers have developed different frameworks to preserve their privacy and control their ledger data. Moreover, existing work based on blockchain addresses the crypto-privacy issues. In this article, the proposed design and a thorough discussion are provided on the current state of blockchain-based healthcare systems.