Reviewerly/exHarmony huggingface dataset:
Image Captioning using Hybrid of VGG16 and Bidirectional LSTM Model

Image captioning is one of the biggest challenges in the fields of computer vision and natural language processing. Many other studies have raised the topic of image captioning. However, the evaluation results from other studies are still low. Thus, this study focuses on improving the evaluation results from previous studies. In this study, we used the Flickr8k dataset and the VGG16 Convolutional Neural Networks (CNN) model as an encoder to generate feature extraction from images. Recurrent Neural Network (RNN) uses the Bidirectional Long-Short Term Memory (BiLSTM) method as a decoder. The results of the image feature extraction process in the form of feature vectors are then forwarded to Bidirectional LSTM to produce descriptions that match the input image or visual content. The captions provide information on the object’s name, location, color, size, features of an object, and surroundings. A greedy Search algorithm with Argmax function and Beam-Search algorithm are used to calculate Bilingual Evaluation Understudy (BLEU) scores. The results of the evaluation of the best BLEU scores obtained from this study are the VGG16 model with Bidirectional LSTM using Beam Search with parameter K = 3 and the BLEU-1 score is 0.60593, so this score is superior to previous studies.
A Novel Generative Adversarial Network for Underwater Image Enhancement

The ocean contains many resources that are vital to the sustainable development of human society. Underwater images provide an important way for people to understand the underwater environment of the ocean. However, due to the uneven medium in water and the scattering effects of light, underwater images usually suffer from some severe degradation problems, such as color distortion and blurring. As such, underwater image enhancement is a very important and critical task in detection of underwater marine objects. To circumvent these problems, a novel generative adversarial network for underwater image enhancement is proposed. In the proposed method, the generator makes use of U-Net network structure and combines CBAM and ASPP to ensure effectiveness of multi-scale feature extraction. The discriminator employs a double-branch structure to guide the generator to generate images from global semantics to local details and enhance local details of the images. Finally, in the quantitative and qualitative analysis, the proposed method is compared with state-of-the-art methods. Comparative studies demonstrate that our proposed method is better than other methods.
Analysis of SQL Database Code Optimization for Short Video Operation Mode based on Artificial Intelligence

This article first proposes modeling based on the SQL database network structure, improving the short video operation mode, processing the short video text characteristics of the operator’s avatar, and using the deep neural network (DNN) in the video scoring stage. Algorithm for score prediction. By introducing the genetic crossover operation based on the probability model into this article, the problem of poor optimization effect in the later stage of the traditional crossover operation is effectively improved, and the algorithm convergence is accelerated; by introducing the genetic mutation operation based on the dynamic parameter control method into this article, the traditional mutation operation is improved In the later stage, the diversity of the population cannot be taken into account while maintaining good local search capabilities.
Beyond weak coupling: non-perturbative string theory

A summary is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
What is the simplest model that can account for high-fidelity imitation?

Abstract What inductive biases must be incorporated into multi-agent artificial intelligence models to get them to capture high-fidelity imitation? We think very little is needed. In the right environments, both instrumental- and ritual-stance imitation can emerge from generic learning mechanisms operating on non-deliberative decision architectures. In this view, imitation emerges from trial-and-error learning and does not require explicit deliberation.
Development of augmented reality video application

The subject of this paper is the development and design solution of an augmented reality application for marketing purposes. The main goal of the study is to examine the functionality of Unity software and Vuforia software development kit, as well as create an application that will allow the user to play video content using augmented reality technology. The application's primary goal is to give the user more information about the Company behind the Augmented Reality target image. The development of the application was preceded by research in the field of immersive technologies and software for Augmented Reality development. The aim of this research is to set up an intuitive system in the form of a mobile application that would identify the image and play and control the video content on the mobile device screen using Augmented Reality technology.
Application: Privacy, Security, Robustness and Trustworthiness in Edge AI

This chapter comprehensively reviews the secure, private and trustworthy learning methods from diverse perspectives. Motivated by the security and privacy issues, this chapter first presents the real data disclosure risks, attack scenarios and vulnerabilities of the current learning systems. Afterwards, the mainstream mitigation methods proposed to use differential privacy, homomorphic encryption, lightweight secure multi-party encryption, blockchain and trusted execution environment are reviewed by introducing the core algorithms and protocols. Following the tone of classical textbook writing style, in each sub-chapter, the advantages and limitations of the current schemes are discussed. Besides, this chapter argues that the adoption of single technique such as cryptograph brings about significant drawbacks. Therefore, the deep fusion of reviewed methods is promising research and practicing route to design secure while efficient learning protocols. The knowledge and insights shared in the chapter can motivate future efforts from our readers.
ONLINE AUCTION SYSTEM IN E-COMMERCE PRODUCTS USING DEEP LEARNING AND DATA MINING

An online auction system is an auction which is held over the internet.It is a popular method for buying and selling products and services.Online Auction System s helps to customer to sell and buy product in best price.This application is used to sell the anything on the website from house.Online auction system is becoming a more an more popular is electronic E-commerce, It Deeply learn becomes a main stream trending in customer to customer such as E-bay.Thousands of people take part in internet auctions everyday, bidding on items from different places.
Saliency Preserving Image Retargeting Using Probability Density

In this paper, we propose saliency preserving image resizing based on probability density. We first obtain an initial saliency map by spectral residual. Then, we obtain the probability density to represent the relationship between common and salient pixels in the saliency map. Based on the probability density, we update the saliency map using a strengthening function. Finally, we perform image resizing based on the saliency map. Experimental results demonstrate that the probability density is very effective in preserving saliency in images and the proposed method achieves good performance in image resizing.
Comparing a Large-Scale Display and an Interactive Projector for One-to-Many Mixed Reality (MR) Remote Collaboration

Mixed Reality (MR) has significant potential to support scenarios of remote collaboration, where distributed team-members need to establish a common understanding. However, most efforts have been devoted to explore one-to-one use-cases. This work describes a one-to-many user study with 16 participants, aimed at understanding how an on-site team-member can be assisted by small groups using: a large-scale display (C1) and an interactive projector (C2) to collaborate and provide guidance during a remote maintenance procedure. Results suggest condition C1 was preferred by the majority of participants, having higher level of social presence and being considered more useful for information understanding, while condition C2 was considered easier to express ideas properly.
Evaluating the Suitability of distinct Handheld Device setups for Augmented Reality Remote Maintenance: A user study

Augmented Reality (AR) has been explored to assist scenarios of remote collaboration. Most studies use Handheld Devices to assist on-site members requiring help. However, the most suitable way to use such devices has not been examined, due to most efforts being put in the development of the technology itself. This work explored three distinct setups during a remote maintenance scenario with 10 participants: HHD; Support Mounted HHD; Wrist Mounted HHD. We report participants’ insights, suggesting the last represents the most adequate condition given the scenario context.
Cross-Domain Identity Authentication Protocol of Consortium Blockchain Based on Face Recognition

A consortium system can leverage information to improve workflows, accountability, and transparency through setting up a backbone for these cross-company and cross-discipline solutions, which make it become a hot spot of market application. Users of a consortium system may register and log in different target domains to get the access authentications, so how to access resources in different domains efficiently to avoid the trust-island problem is a big challenge. Cross-domain authentication is a kind of technology that breaks trust islands and enables users to access resources and services in different domains with the same credentials, which reduces service costs for all parties. Aiming at the problems of traditional cross-domain authentication, such as complex certificate management, low authentication efficiency, and being unable to prevent the attack users’ accounts, a cross-domain authentication protocol based on face recognition is proposed in this paper. The protocol makes use of the decentralized and distributed characteristics of the consortium chain to ensure the reliable transmission of data between participants without trust relationships, and achieves biometric authentication to further solve the problem of account attack by applying a deep-learning face-recognition model. An asymmetric encryption algorithm is used to encrypt and store the face feature codes on the chain to ensure the privacy of the user’s face features. Finally, through security analysis, it is proved that the proposed protocol can effectively prevent a man-in-the-middle attack, a replay attack, an account attack, an internal attack, and other attacks, and mutual security authentication between different domains can be realized with the protocol.
ADAPTATION OF FRESHMEN TO THE CONDITIONS OF THE INSTITUTION OF HIGHER EDUCATION

The article highlights the main results of the study of the issue and problems of adaptation of first-year students to the conditions of study in a higher education institution. Attention is paid to the difficulties that arise during the residence of first-year students in a dormitory. Therefore, they need additional attention and carrying out additional forms of work with them on adaptation to the conditions of study in a higher education institution. The directions of work on solving issues regarding discipline compliance by first-year students in the conditions of a higher education institution are proposed. The results are presented regarding the interest of involvement of first-year students in forms of extracurricular activities. These are forms of self-help, volunteerism, student self-government, involvement in group work, visits to theaters, cinemas, and excursions. In addition, the role of the curator in the process of adaptation of first-year students to the conditions of study at a higher education institution is defined, and the components of the curator's work plan with the academic group are revealed. It was determined that the main goal of the curator's work with first-year students is to create favorable pedagogical conditions for their successful socialization and adaptation. It was noted that the quality of assimilation of knowledge, abilities and skills of the future profession, mastery of the profession is affected by the professional adaptation of the first-year student. Successful adaptation of first-year students depends on the active involvement of all subjects of educational work in this process. The entrant's conscious choice of specialty affects the adaptation process and manifests itself in the presence of positive dynamics in the adaptation process, which, in turn, manifests itself in a decrease in the level of students' experiences, awareness of the problems overcome in the adaptation process, and participation in the public life of the university. In our opinion, the formation of such basic character traits as purposefulness, confidence and determination in students is relevant, which will enable students to better overcome difficulties in learning, communication, finding new acquaintances, and in the future also in professional activities. Key words: adaptation, institution of higher education, first-year student, social adaptation, difficulties in adaptation.
Procedural learning in virtual reality: The role of immersion, interactivity, and spatial ability.

Although there is much enthusiasm for using virtual reality (VR) in training and education, research findings on the benefits of VR over traditional learning environments are mixed.This disconnect in the literature may be because the cognitive mechanisms that underlie learning in VR have not been addressed systematically.This research explored immersion and interactivity, two key features of VR that set it apart from traditional training approaches.In addition, the impact of spatial ability (SA) on learning outcomes was also assessed.In this experiment, 83 college students learned a maintenance procedure in a virtual environment (VE).Students were assigned randomly to one of three between-subjects training groups.In the Desktop group, students used a desktopbased environment and interacted with it using a mouse and keyboard.In the VR-voice group, students used a VR-based environment and interacted using voice-based commands, while those in the VR-Gesture group interacted using gesture-based commands.To measure learning, students completed a written recall test.There were no significant differences in learning outcome across the three groups, but SA was a significant moderator.Overall, those with high SA outperformed those with low SA in the Desktop condition; however, this effect was not significant in the VR conditions.In fact, those with low SA performed best in the VR-gesture condition compared to the VR-voice or Desktop training.These results suggest that VR environments that incorporate gesture-based interactions may help individuals with low SA better understand the content of the lesson.
THE DETECTION OF FACE RECOGNITION AS EMPLOYEE ATTENDANCE PRESENCE USING THE YOLO ALGORITHM (YOU ONLY LOOK ONCE)

face detection is a fundamental and important process in the field of facial recognition. The aim of this face detection was to determine the presence and mark the position of the face through an image called a bounding box. The problem that we often encounter in attendance machines is that it is often difficult and takes a few seconds to perform facial recognition with a low level of accuracy. Therefore, an attendance system that is faster and more accurate in recognizing faces is needed with the aim of increasing better accuracy. This research was undertaken by applying the You Only Look Once (yolo) algorithm with several test scenarios to see the performance generated by the system, because yolo is one of the fastest and most accurate methods for object detection and even exceeds 2 times the capabilities of other algorithms. The yolo (you only look once) algorithm is an architecture of deep learning and an algorithm developed to detect an object in real-time. Regarding to the classification performance measurement from the training data, it indicated that the accuracy value has reached 90% thus it can be concluded that the system can work wellt.
Applying Word Embedding Methods on Customers for Classification

Nowadays, machine learning is used in almost every field. In machine learning, classification is one of the most fundamental methods. And the success of a classification model highly depends on feature selection. Generating new features through feature engineering also plays a critical role in the success of the models. In this study, several classification models have been built on a fraud detection dataset. Even though it is a natural language processing method, word embedding methods are applied to customers and a customer space is created. Afterward, customer vectors in the generated space are included in the classification model as features, so that a better customer representation is added to the models. The model in which embedding methods are contained in the classification is compared with other models. Observing the results, the customer embedding method has a constructive outcome on the success of the models.
SUBSTITUTIONAL MODEL OF THE SOIL BASED ON ARTIFICAL NEURAL NETWORKS

This paper presents an interpretation of the results of measurement of specific soil resistivity by means of artificial neural networks. The model based on artificial neural networks replaces the soil which can be physically considered a two-layer medium with a vertical change of the specific electric resistivity and a horizontal boundary line between the layers. Learning of the neural network was performed under supervision using the input dataset obtained by means of a very accurate theoretical model of the double-layer soil. The proposed algorithm that approximates non-linear soil properties using the artificial neural network is reliable in assessment of the soil parameters and specific electric soil resistivity. Application of the substitutional model of the soil based on neural networks is demonstrated by a realistic example, determination of parameters of the double-layer soil from the measured data obtained by the Wenner technique for measuring the specific soil resistivity. For simplicity of presentation and model comparability, the current probes (poles) are replaced by the ball electrodes, i.e. spot fi eld sources. The results obtained are analytically and graphically presented and discussed.
Efficient Integrity-Tree Structure for Convolutional Neural Networks through Frequent Counter Overflow Prevention in Secure Memories

Advancements in convolutional neural network (CNN) have resulted in remarkable success in various computing fields. However, the need to protect data against external security attacks has become increasingly important because inference process in CNNs exploit sensitive data. Secure Memory is a hardware-based protection technique that can protect the sensitive data of CNNs. However, naively applying secure memory to a CNN application causes significant performance and energy overhead. Furthermore, ensuring secure memory becomes more difficult in environments that require area efficiency and low-power execution, such as the Internet of Things (IoT). In this paper, we investigated memory access patterns for CNN workloads and analyzed their effects on secure memory performance. According to our observations, most CNN workloads intensively write to narrow memory regions, which can cause a considerable number of counter overflows. On average, 87.6% of total writes occur in 6.8% of the allocated memory space; in the extreme case, 93.9% of total writes occur in 1.4% of the allocated memory space. Based on our observations, we propose an efficient integrity-tree structure called Countermark-tree that is suitable for CNN workloads. The proposed technique reduces overall energy consumption by 48%, shows a performance improvement of 11.2% compared to VAULT-128, and requires a similar integrity-tree size to VAULT-64, a state-of-the-art technique.
AsyncSequence

In Swift, we have the concept of Sequences. Formally, Sequence is a protocol that types that need sequential and iterated access to their elements depend on. Arrays conform to Sequence, because you can iterate through them via a for-in loop, the forEach method, and with higher-order functions such as filter, map, and reduce. You can simply access their elements with a numbered index such as myArray[2]. Dictionaries are very similar. You can iterate over their keys and values in the same ways as an array, and to access an element in a dictionary, you can simply call myDictionary[myHashableKey]. Sets behave similarly, and you can even implement your own types that conform to Sequence.
Estimating the replicability of technology education research

Technology education research is a growing field, with the rate of growth increasing over the last 2 decades. As the field grows, it is paramount that credibility is maintained in published findings. To date there is no evidence to suggest a lack trust is warranted, however in the midst of the replication crisis there is need to ensure continued rigour. This article presents a z-curve analysis of the replicability of quantitative research in technology education since 1983 using statcheck for automated data extraction. The results indicate that authors often mis-report p-values, typically due to rounding errors, with a small percentage (1.59%) of inconsistently reported p-values leading to decision errors in terms of statistical inference. With respect to replicability, overall it is estimated that 55.7% of reported quantitative results in technology education would replicate, however since 2020 this estimate appears to be increasing. These results do not indicate specific findings which are likely or unlikely to replicate, but do suggest a need to invest effort in identifying studies which would have a high value in being replicated, particularly in the timeframe of work published from 2010 to 2020.
Measuring influence through quoting novel words

How quickly inventors of new words get others to use their newly created words is an excellent measure for the persuasive power of the inventor. More generally, the speed with which new concepts are picked up by others is an efficient metric for the influence that the inventor of the new concept has within a community. Using Natural Language Processing (NLP) this influence can easily be measured.
Gramian Angular Field Transformation-Based Intrusion Detection

Cyber threats are increasing progressively in their frequency, scale, sophistication, and cost. The advancement of such threats has raised the need to enhance intelligent intrusion-detection systems. In this study, a different perspective has been developed for intrusion detection. Gramian angular fields were adapted to encode network traffic data as images. Hereby, a way to reveal bilateral feature relationships and benefit from the visual interpretation capability of deep-learning methods has been opened. Then, image-encoded intrusions were classified as binary and multi-class using convolutional neural networks. The obtained results were compared to both conventional machine-learning methods and related studies. According to the results, the proposed approach surpassed the success of traditional methods and produced success rates that were close to the related studies. Despite the use of complex mechanisms such as feature extraction, feature selection, class balancing, virtual data generation, or ensemble classifiers in related studies, the proposed approach is fairly plain -- involving only data-image conversion and classification. This shows the power of simply changing the problem space.