Reviewerly/exHarmony huggingface dataset:
Face Swapping Method Integrating Multi-Level Attributes and Attention Mechanism

The face swapping methods available only use the posture and expression of the target images to guide the face swapping process, which often ignores other attributes such as background and lighting. Moreover, the swapped faces are poorly fused with the target images. Thus, a face swapping method combining multi-level attributes and attention mechanism is proposed. At the stage of extracting target attributes, a multi-level attribute encoder is designed based on the U-Net structure, using multi-level cascaded convolutional and deconvolutional blocks and inter-layer connections to accurately and comprehensively extract the expression and background attributes of the target images, preserving more detailed information. Meanwhile, in the swapped faces generation stage, a generator is designed that incorporates the attention mechanism to adaptively adjust the effective area of the integration of source face features and target attributes, so that the swapped faces are more consistent with visual mechanism. Experimental results on FaceForensics++ show that compared with the DeepFaceLab method, the structural similarity between the swapped faces and the target images is improved by 6.73%, and the differences in head posture and facial expression are reduced by 1.026 and 0.491, respectively. Proposed method retains the source face features better, preserves a greater degree of fidelity to the target image attributes, and achieves a good swapped effect.
Region-Based Multiple Object Tracking with LSTM Supported Trajectories

Object Tracking is the growing field in computer vision with its demands in various areas in monitoring and surveillance. Areas of surveillance can be improved with proper and efficient trackers that can ensure people’s safety on roads, the safety of children in school, and in many other areas. Object tracking is a super-set of elements object classification, object detection, etc. Most of the work done consists of tracking based on visual features, so we have worked on region-based features. In this context, a method is proposed for tracking, based on region features extracted with the help of intersection over union and prediction of trajectories with the help of LSTM in case of occlusion. Comparison for the same is being carried out with the traditional centroid tracking algorithm.
THE ROLE OF THE TASK DESIGNER IN IMPROVING THE QUALITY OF PRIMARY SCHOOL EDUCATION

The article offers an analysis of psychological and pedagogical literature on the problem of the formation of value-semantic attitudes in order to argue the relevance of this problem and the need to develop a task designer in this direction to improve the quality of teaching younger schoolchildren.
The Impact of Non-immersive Virtual Reality Technologies on Consumers' Behaviors in real estate: A Website's Perspective

With the development of virtual reality (VR) technologies, an increasing number of studies have recognized that virtual reality (VR) technologies increased consumer experience and made life convenient. In real estate platform, the effectiveness of VR technologies on consumer behaviors from the website perspectives remains ambiguous. Consequently, this study investigates the impact of the non-immersive VR technologies experience provided by real estate website on the consumers' behaviors. By crawling data from the website, we capture consumers' viewing behaviors when presented the property through static photographs and 3D Tour. This study highlights the advantages of non-immersive VR technologies in increasing the number of consumers' behaviors significantly from the website perspective.
Bidirectional Decoding Tacotron for Attention Based Neural Speech Synthesis

Attention-based neural text-to-speech (TTS) has become increasingly popular because of its end-to-end network architecture and impressive performance comparable to human recordings. However, existing approaches usually adopt a unidirectional decoding framework generating the target spectrum from left to right, which cannot take advantage of reverse target-side contexts from right to left. To mitigate the problem, we present a bidirectional decoding speech synthesis network based on the well-known Tacotron2. In particular, our model first employs a backward decoder to predict the spectrum from right to left, conditioned on the output states of the text encoder. Then, the forward decoder generates the spectrum from left to right, attending to both encoder outputs and the context hidden states from the backward decoder. With this architecture, our bidirectional decoding Tacotron2 can exploit both backward and forward information to promote the performance. Experiments with objective and subjective evaluations on LJSpeech have been conducted to demonstrate the effectiveness of our proposed method.
Multi-scale Lightweight Neural Network for Real-Time Object Detection

Deep learning-based object detection methods have been widely used in computer vision recently. Many previous works on object detection improve performance by utilizing complex algorithms and architectures or involving expensive calculations. But they often ignore the real-time requirement of applications such as autonomous driving. Our work is to design a lightweight network for real-time inference on edge devices. Considering the inference speed of one-stage object detection is significantly faster than that of two-stage object detection, we propose a one-stage Multi-scale Lightweight Neural Network (MLNet), which integrates various classification and detection features on multiple scales. The proposed neural network contains parallel multi branches, and each branch has different output feature mapping sizes. We also propose lightweight feature extraction blocks used in the network branches to improve performance and reduce computations. A lightweight detection head is used to reduce the inference time further. Experimental results on COCO datasets demonstrate that MLNet achieves 28.7% AP on COCO with only about 2.1M parameters and shows its superiority compared to the five widely used state-of-the-art object detection methods.
Combined with Pyramid Split Attention and Multi-Scale Feature Learning Network for Single Image Deraining

The difficulty of single image deraining using deep learning method lies in how to extract feature information of rain streaks. To effectively extract discriminant information, we propose a network for single image deraining, termed dense network of combing pyramid split attention with multi-Scale feature learning (DPMNet). We utilize improved DenseNet based on pyramid split attention (PSA) module as the overall framework of DPMNet, where each dense layer is composed of a multi -scale feature learning (MSFL) module. The PSA module aims to learn important features using the attention mechanism. The MSFL module aims to learn the features of different scales, where re-parameterization VGG (Rep VGG) block is used for multi-scale feature extraction and mobile inverted bottleneck (MB) convolution is used for multi-scale feature fusion. By introducing pyramid split attention and re-parameterization VGG skillfully, the proposed method can obtain distinguishing rain features efficiently. Extensive experiments on benchmark datasets as well as specific real datasets demonstrate that the proposed method outperforms recent state-of-the-art methods.
Decision Making Bot Algorithm in Real-Time Strategy Games

This paper discovers a cheap decision-making method by using data-mining techniques to find a sequence of relatively optimal sub-actions under a given truncated condition from the information of previous decision-making experts' complex actions under different conditions. This method can find a local-maximal solution without massive data and computation. Also, the provided conditions can be imperfect, which means data fragments and limited outliers are accepted. This paper then applied this method to implement a bot in StarCraft 2, a classical RTS game developed by Blizzard Entertainment.
THE ROLE AND SIGNIFICANCE OF ETHICS IN PROFESSIONAL ACTIVITIES

Professional morality, as already mentioned, consists primarily of such activities, the object of which is a person. Undoubtedly, unique situations, difficulties, and contradictions arise in working with people, which must be resolved and overcome in the
Performance Analysis of Image Enhancement Techniques for MRI Brain Images

In this research article, image enhancement techniques like histogram equalization (HE), dualistic sub-image histogram equalization (DSIHE), & exposure-based sub-image histogram equalization (ESIHE) techniques are utilized for brain image contrast enhancement. Medical images like mammogram, magnetic resonance imaging (MRI), and X-ray play very significant role in early detection & effective diagnosis of diseases. Thus, it becomes very important to have clear, improved quality image. Mostly, medical images suffer from different issues like noise, bad contrast, so it becomes extremely important to remove noise amplification and improve image contrast for proper treatment of patients. Image enhancement techniques are employed to improve various parameters of image like visual quality, entropy, brightness, and contrast to make it suitable for different applications. ESIHE technique is the advance technique over other traditional methods whose performance is compared based on visual quality, entropy, peak signal-to-noise ratio (PSNR), & absolute mean brightness error (AMBE). Experimental result shows that ESIHE gives the improved performance compared to HE & DSIHE.
Artificial Intelligences-Based Approaches for Generating Image Caption

Most of the visual information presented to humans doesn’t have a description associated with it, but humans can generally understand the visual information without any detailed description accompanying them. But for a machine to perform the same task of interpreting the same images and generate description in a Natural language form is a hard task. For a machine, the process involves two basic human tasks of seeing things and understanding them. In computing, these problems come under the domains of Natural Language Processing and Computer Vision. Therefore creating descriptions of the Image we need a combination of both tasks to be performed. The image description process consists of two parts: first part is to detect the important objects, features attributes, and the relationships between them in an image that comes under the field of Computer Vision, and then it should also generate semantically and syntactically accurate phrases, sentences which the area of Natural Language Processing. As this task of Describing images with human-readable language is a cognitive task the recent progress in area of Machine and Deep learning which are the subset of Artificial Intelligence in which computer algorithms try to copy and adapt the workings of the human brain in processing data and has accelerated the study of this challenging problem of image description. In this paper, the basic techniques and ways to do image captioning are discussed.
Trend of the Use and Investment of Blockchain Technology in the Banking Sector in Ecuador

Technological advances used in the banking sector have allowed the coupling of various technological and computer tools, giving rise to Blockchain. This research presents a vision of the components, the architecture, the platforms, and the algorithms used and finally the frequency of their use. This research highlights the evolution of Fintech in Latin America, and links up these statistics to the reality of the banking sector in Ecuador; it is also described the performance indicator of the investment made in three of the main banks in this country and the amounts invested in technology are shown as evidence that they are also part of the digitalization process of the financial industry.
Integration of a Virtual Object on Formative Object Oriented Programming for the Emavi Military Aviation School Cadets

Learning and acquiring a knowledge base on object-oriented programming (OOP), is an undertaking that future computer engineers must embrace due to the increasing complexity of the apposition of Information and Communication Resources (ICR) as per the quotidian activities of contemporary societies, thus begetting the need to construct knowledge in a more effective and direct way that guarantees the tutelage of the future Colombian Air Force officers in the computer science field. Throughout the elaboration of the article, the outcome of said formation was observed by means of an experimental research under a coed (qualitative-quantitative) method wherein the lore of Objectoriented Programming (OOP) was gauged by the development of the Virtual Object (VO) which complied with the knowledge accessibility mission, fostering decentralized learning aimed towards the formation of competencies of the teaching staff, athwart the implementation of Smart Learning as a primordial factor in the creation of an oriented Virtual Object.
Digital teaching tools as a means of improving the effectiveness of teaching foreign languages in non-linguistic universities

The new information stage of the development of human society sets a new vector for the pedagogical science development and makes university teachers use digital pedagogical tools in teaching foreign languages which include: electronic textbooks, MOOCs, educational feature films, video and audio podcasts, online tests, and online educational forums. The authors consider various types of video materials offered on the Internet; provide a theoretical justification for the need to use these materials in the process of teaching foreign languages in non-linguistic universities. The paper identifies the specifics of organizing the teaching process using these video films. The article presents the results of an empirical study of students ’ participation in an experiment, during which they watched authentic feature films produced in the UK, USA and Australia independently on the Internet in order to identify differences in the British, American and Australian versions of the English language. The results of the study prove the high efficiency of using video materials in the educational process, which are easy to access on the Internet thanks to the development of information technologies.
TDViT: Temporal Dilated Video Transformer for Dense Video Tasks

Deep video models, for example, 3D CNNs or video transformers, have achieved promising performance on sparse video tasks, i.e., predicting one result per video. However, challenges arise when adapting existing deep video models to dense video tasks, i.e., predicting one result per frame. Specifically, these models are expensive for deployment, less effective when handling redundant frames, and difficult to capture long-range temporal correlations. To overcome these issues, we propose a Temporal Dilated Video Transformer (TDViT) that consists of carefully designed temporal dilated transformer blocks (TDTB). TDTB can efficiently extract spatiotemporal representations and effectively alleviate the negative effect of temporal redundancy. Furthermore, by using hierarchical TDTBs, our approach obtains an exponentially expanded temporal receptive field and therefore can model long-range dynamics. Extensive experiments are conducted on two different dense video benchmarks, i.e., ImageNet VID for video object detection and YouTube VIS for video instance segmentation. Excellent experimental results demonstrate the superior efficiency, effectiveness, and compatibility of our method. The code is available at https://github.com/guanxiongsun/vfe.pytorch.
Handcrafted Mobility: The Field Study into DIY Vehicles and Practices in the Russian Northern Periphery

This pictorial presents designerly immersion into DIY activities on catering to local mobility needs in the periphery areas of the Russian North. In two case studies, we observe and document different manifestations of user creativity by necessity, namely rethinking available materials, tools and competencies. We combine photos and drawings to grasp the vivid dynamic relationship between humans and technologies within the ever-changing and challenging environment. Finally, we frame the value of field trips for designers in the context of design education as understanding DIY/making as a form of autonomous life support in an extreme environment.
Application of Apriori Algorithm in Professional Elective Course Data Mining for University

College students lack the purpose of professional elective course selection, and do not consider whether the course is helpful to future employment. In addition to the course content, teachers' engineering experience and course scheduling semester have an impact on the teaching effect of the course. It is biased to recommend students to choose professional elective courses only based on the experience of the dean or responsible professors. To solve this problem, we apply Apriori algorithm for elective course data mining to provide an objective evaluation. We takes students' course selection records and employment situation including further education as a transaction, and uses Apriori algorithm to give the strong association rules between professional elective courses and employment. Based on the data of one year's graduates, we recommend nine professional elective courses from sixteen courses to college students. This assisted the dean in his decision-making. In addition, it can also promote the continuous improvement of unselected courses.
Fall Detection System for Depression Angle RGB Image based on Human Skeletonch

In recent years, Taiwanese society is gradually aging, and the existence of fall detection systems is crucial to protect the health and safety of the elderly in their daily lives. In this paper, we used the Openpose human skeleton recognition algorithm to extract the 2D human skeleton information from RGB images and took actions to recognize the 2D human skeleton information by the LSTM. In order to solve the problem of incomplete and inaccurate human skeleton information extracted from the top angle camera, this paper proposes an algorithm that uses an object recognition model to assist in extracting human skeleton information to improve its skeleton recognition rate, and then enhance the accuracy of the subsequent LSTM model to obtain more accurate fall detection results.
A New Dynamically Changing Attack on Review Fraud Systems and a Dynamically Changing Ensemble Defense

A number of companies sell "fake" reviews of products for a fee to unscrupulous customers. To do this, they operate a set of sockpuppet accounts. We use the term SockFarm to refer to such companies. We propose SockAttack, a way that such shady companies can judiciously create fake accounts in order to minimize detection. We show that SockAttack compromises the F1-score of 4 well known review fraud detectors more than baselines on real world datasets (up to 28.8%). We then propose SockDef, an algorithm to mitigate the efficacy of SockAttack and show that SockDef mitigates the impact of SockAttack (up to 63.6% w.r.t. F1-score).
Exploring the Effect of Virtual Agent Type on Perceived Hedonic and Utilitarian Beliefs of an End User

Online settings have become more commonplace for interactions. Currently, a common-place interaction is with a chatbot of some kind when traversing the web, however, chatbots offer a limited amount of communication channels for social interactions. These limitations could possibly be addressed by using social robots as alternatives for interacting. The present study examines the effects of a real visual component in an online viewing task on participants’ perceived beliefs (hedonic and utilitarian) of a virtual agent. Perceptual judgments were collected after actively viewing a task with one of the two agents (chatbot or online social robot). The focus of the study was to determine whether social robots offer an increased hedonic experience compared to chatbots. Additionally, utilitarian factors were explored. Results indicated that the online social robot offered an overall better experience being perceived as more hedonic as well as being more perceived as easier to use and adaptable.
Features of the part of a competent approach to teaching physicists based on the use of songs in learning English

The use of modern digital information technologies significantly increases the opportunities for distance learning. Distance learning technologies make it possible to improve the level of education for a wide range of the population, regardless of their social status, age and meta of residence. The use of the Internet in distance learning in English allows you to make the learning process comfortable and convenient for students. The distance learning form has a number of disadvantages, which are related to the quality control of the training of students and the lack of direct contact between students and teachers. The use of English-language songs in learning English helps to increase the vocabulary of students and better assimilation of grammatical structures by them. Songs help to reduce the psychological burden on students, improve articulation and improve the quality of pronunciation of sounds. The study of foreign languages is important for the training of specialists in the field of physics.
Public Data Assisted Differential Private Deep Learning

Deep neural networks are capable of making classification or prediction using large amounts of labeled data. When there's no or only a few labeled data available, domain adaptation can be adopted to transfer a model from the source domain where many labeled data are available to the target domain with few labeled data. In practice, in addition to private data, some public data are generally available and can be utilized as source domain. However, due to the exposure of the training model, the information of the private data may be potentially compromised. In this paper, we propose public data assisted differentially private deep learning methods which generalize well across different distributions of the private and public data. Specifically, two different methods are presented for different scenarios: unsupervised learning and supervised learning. The proposed methods can leverage the public data for higher performance while providing privacy guarantee for the private dataset. Empirical study on standard benchmark datasets validates the superiority of our approaches.
Research on Intelligent Recognition Algorithm of Hole Array Code Based on Image Processing

It is very important to accurately identify the materials on the production line in the production application scenario of modern industrial Internet of Things. In the intelligent manufacturing assembly line of pressure vessels, it is difficult to identify the cylinders of pressure vessels in real time. In order to identify the gas cylinders on the intelligent production line accurately and quickly, the hole array code is used to identify the gas cylinders and the intelligent image processing algorithm based on OpenCV is used to locate the hole array code on the gas cylinders, and the result of the hole array code identification on the gas cylinders is converted into a binary marking data. By using the circle detection and intelligent identification algorithm in OpenCV, the identification accuracy and real-time detection efficiency of positioning holes are ensured, and the final verification accuracy reaches 99.2%.
Reinforcement Learning in Social Media Marketing

In this chapter, the authors describe an architecture for reinforcement learning in social media marketing. The rule bases used for action selection within the architecture build upon many-valued (fuzzy) logic. Action evaluation and internal learning is based on neural network like structures. In using variables measuring the effect of advertising, we must understand direction of influence between advertiser, owning the content of the advertisement, and advertisee, as the target of an advertisement, and as facilitated by social media marketing. Examples are drawn from Facebook marketing.