Reviewerly/exHarmony huggingface dataset:
Distance learning in professional education: topical issues

The importance and necessity of introducing distance learning is due to the global situation with coronavirus infection since the beginning of 2020, which resulted in the emergency transition of educational institutions to online mode. The active use of distance learning technologies has changed the concept of the entire education system, its organizational and substantive aspects [4, p. 293]. Therefore, today, studies devoted to the experience of using and interacting between the subjects of the educational process, namely, technologies of distance learning in vocational education, optimization of organizational, substantive and methodological issues in this area are demanded. The research problem is to reveal the actual aspects of distance learning in vocational education. The purpose of the research is to identify topical issues of distance learning in vocational education in modern conditions. The research methods used are analysis, synthesis, generalization. The experimental base is Maykop State Technological University. The research results: the concept of «distance learning» has been disclosed; the concept of distance learning in vocational education has been described; new technological educational trends have been described; the problems of implementation of distance education in modern conditions have been identified. Key findings. Distance learning refers to innovative educational technologies that ensure the availability and quality of the educational process at a distance online; the means of implementing distance learning are learning platforms, Internet resources; distance learning has contributed to the emergence of new technological educational trends: cloud services, massive open online courses, adaptive learning, behavioral analytics, etc. Topical issues of distance learning in vocational education are the quality of professional training of future specialists in a distance format, the lack of generally accepted standards for distance organization and the procedure for assessing students' knowledge, technical difficulties, etc.
FakeFilter: A cross-distribution Deepfake detection system with domain adaptation

Abuse of face swap techniques poses serious threats to the integrity and authenticity of digital visual media. More alarmingly, fake images or videos created by deep learning technologies, also known as Deepfakes, are more realistic, high-quality, and reveal few tampering traces, which attracts great attention in digital multimedia forensics research. To address those threats imposed by Deepfakes, previous work attempted to classify real and fake faces by discriminative visual features, which is subjected to various objective conditions such as the angle or posture of a face. Differently, some research devises deep neural networks to discriminate Deepfakes at the microscopic-level semantics of images, which achieves promising results. Nevertheless, such methods show limited success as encountering unseen Deepfakes created with different methods from the training sets. Therefore, we propose a novel Deepfake detection system, named FakeFilter, in which we formulate the challenge of unseen Deepfake detection into a problem of cross-distribution data classification, and address the issue with a strategy of domain adaptation. By mapping different distributions of Deepfakes into similar features in a certain space, the detection system achieves comparable performance on both seen and unseen Deepfakes. Further evaluation and comparison results indicate that the challenge has been successfully addressed by FakeFilter.
Optimal Virtual Machine Provisioning in Cloud Computing Using Game Theory

Virtualization of resources on the cloud computing allow fast and easy scaling of infrastructure to achieve a specified QoS. This will undoubtedly improve the allocation efficiency of physical machines in large-scale data centers. In fact, virtualization technologies not only have a direct effect on the performance and energy consumption but also can reduce the operating costs considerably. As there is fierce competition among cloud providers, there is an urgent need to adopt an elastic resource management system and an optimal pricing strategy as well. Such a strategy is designed principally to satisfy the requirements of a given service and simultaneously increase its associated profit margins. This study highlights why game theory is a very useful tool to analyze the impact of both energy efficiency and response time on virtual machine (VM) provisioning. To this aim, we present a formal policy that ensures fairness in the resource sharing and also takes into account the reasonable expectations market participants. More precisely, we rely on a non-cooperative game model in choosing the right provisioning scheme, and then discuss its equilibrium and stability. In a duopoly market, we conduct some numerical experiments to illustrate the efficiency and performance of the proposed approach and its proof of convergence with a certain number of iterations.
Implementation Online Lectures in Covid-19 Pandemic

The purpose of this study was to determine students' perceptions regarding online lectures that were implemented during the Covid-19 pandemic, and the obstacles during the implementation of online lectures. This research is a descriptive study using a survey method. The sample in this study were 93 students majoring in mathematics education at Musamus University consisting of 35-second semester students, 30 fourth semester students, and 28 sixth semester students. The sampling technique in this study used saturated samples. This research was conducted in the even semester of the 2019/2020 academic year. The data collection technique was carried out by distributing questionnaires, of the 93 questionnaires distributed to students via Google Form, only 74 students filled out the questionnaire due to Internet connection constraints. During online lectures as many as 49 or 66.2% conduct lectures at home, 15 students or 20.3% conduct lectures at family/friends' homes, as many as 7 students or 9.5% conduct lectures at boarding houses, and 3 or 4 students. 1% who conduct online lectures in rice fields/gardens. Electronic devices used by students in online lectures are smartphones, and laptops. The e-learning application chosen by students when taking online lectures was 64 students or 86.5% chose google classroom, 12 students or 16.2% chose zoom, 9 students or 12.2% chose google meet, 4 students or 5.4 % chose Moodle, 3 students or 4.1% chose Edmodo, and WhatsApp, and 1 student or 1.4% chose Quizizz.
Ω-GAN: Object Manifold Embedding GAN for Image Generation by Disentangling Parameters into Pose and Shape Manifolds

In this paper, we propose Object Manifold Embedding GAN (Ω-GAN) to generate images of variously shaped and arbitrarily posed objects from a noise variable sampled from a distribution defined over the pose and the shape manifolds in a vector space. We introduce Parametric Manifold Sampling to sample noise variables from a distribution over the pose manifold to conditionally generate object images in arbitrary poses by tuning the pose parameter. We also introduce Object Identity Loss for clearly disentangling the pose and shape parameters, which allows us to maintain the shape of the object instance when only the pose parameter is changed. Through evaluation, we confirmed that the proposed Ω-GAN could generate variously shaped object images in arbitrary poses by changing the pose and shape parameters independently. We also introduce an application of the proposed method for object pose estimation, through which we confirmed that the object poses in the generated images are accurate.
Click-Through Rate Prediction Using Feature Engineered Boosting Algorithms

Click-Through Rate (CTR) prediction plays a critical role in online advertisement campaigns and recommendation systems. Most of the state-of-the-art models are based on Factorization Machines and some of these models try to feed mapped field features to a deep learning component for learning users' interests by modelling feature interactions. Deploying a model for CTR is an online task and should be able to perform well with a limited amount of data and time. While these models are very good at prediction inferences and learning feature interactions, their deep component needs a vast amount of data and time and does not perform well in limited situations.In a recent article, a combination of boosting algorithms with deep factorization machines (XDBoost algorithm) has been proposed. In this paper, we use a boosting algorithm for prediction inference with limited raw data and time. We show that with an appropriate feature engineering and fine parameter tuning for a raw boosting model, we can outperform XDBoost method and get better results. We will use exploratory data analysis to extract the main characteristics of the dataset and eliminate the redundant data. Then, by applying grid search scheme, we select the best values for the hyperparameters of our model.
High dimensional decision making, upper and lower bounds

Abstract A decision maker’s utility depends on her action a ∈ A ⊂ R d and the payoff relevant state of the world θ ∈ Θ . One can define the value of acquiring new information as the difference between the maximum expected utility pre- and post information acquisition. In this paper, I find asymptotic results on the expected value of information as d → ∞ , by using tools from the theory of (sub)-Gaussian processes and generic chaining.
Cognitive Learning Assessment Based on FAHP and RSM: A Case Study of Introduction to Network course

With the progress and innovation of digital technology, people's knowledge acquisition, information exchange, and communication modes have changed significantly. The universality of emerging technology media enables instructional designers to use digital media to provide different teaching materials and teaching methods to meet the needs of individual learners according to their differences. Accordingly, this study takes the Introduction to Network course as an example, proposes a Fuzzy Analytic Hierarchy Process combined with Rule-Space Model to guide and analyze students' learning status, and imports Microsoft Technology Association international certification as empirical analysis. The study revealed that, the experts believe that “understanding protocols and services” is the most important part of the basic knowledge of the Internet, followed by “understanding network infrastructure” and “understanding network hardware.” In the section on learning performance, the average progress rates of two class were 69.44% and 62.20%, respectively, the students' scores were significantly improved. In the section on obtaining Microsoft Technology Association certification, the passing rates were 93.44% and 93.10%, which were significant improvements in certification passing rate. In the future, this study can be applied to other fields to improve the relative learning effect and make it broader and more diversified.
Is General Purpose Sensing a Pipe Dream?

Smart spaces equipped with sensors and combined with learning are becoming increasingly popular for many ubiquitous computing applications. However, application performance depends on many factors, including the number of sensors deployed, the phenomena they measure, their placement throughout the space, and spatio-temporal human-activity distribution characteristics. This work examines the sensitivity of activity-recognition models on the placement and distribution of multi-sensing measurement points in space. We present the results of an empirical study, whereby multi-sensing measurement units are placed throughout a room and train models to identify the type of human activity they observe. Our results show that while our models achieve 95% accuracy, performance degrades with distance and can be highly dependent on unanticipated sensor types. Certain phenomena trigger sensors that are difficult to anticipate a priori, supporting the notion that multi-sensing is useful for general activity recognition and improves robustness. We also show that the pattern of triggered sensors may not be consistent for the same event. Our results suggest designing more adaptive classification models and can guide the design of future cyber-physical systems to capture human activities.
Feature Selection in Multi-label Classification based on Binary Quantum Gravitational Search Algorithm

Unlike a single-label supervisor dataset where each instance is assigned to one class label, in multi-label datasets, several class labels are assigned to each instance, which makes it difficult to build an accurate and comprehensive model from this dataset. In this study, a memetic algorithm for feature selection in a multi-label dataset is proposed. The principal innovation of this study is the offer of a novel local search algorithm which, in collaboration with binary quantum-inspired gravitational search algorithm (BQIGSA), forms the main framework of the proposed memetic algorithm. The main invention of the proposed local search algorithm is to build a number of neighbors for a solution using the prior knowledge vector and the posterior knowledge vector to select effective features and remove useless and irrelevant features. The results of implementing the proposed algorithm and comparing these results with similar works show that the proposed method in most cases leads to better results.
Fused Photo: Augmented Reality Staging for Photorealistic Visualization in Online Home Retail

Retail experiences have started adopting Augmented Reality (AR) as a tool to let customers see products in their space before committing to a purchase. Although online retailers use high resolution 3D models to create onsite imagery, limitations in the rendering capabilities of phones require them to optimize models for real-time use, diminishing the visual quality of the experience and widening the gap between what the customer thinks they are buying and what they get. We present Fused Photo, a system that leverages AR to capture the customer’s spatial context and recreate it in a remote rendering environment that then composites photorealistic images of products onto their space. Results show that Fused Photos outperform other AR visualizations in the market both in realism and ability to represent a product’s appearance, while being comparable in quality to photorealistic renders staged manually by artists.
Semantic-Aware Unpaired Image-to-Image Translation for Urban Scene Images

Unpaired image-to-image (I2I) translation methods have been developed for several years. Present methods do not take into consideration semantic information of the original image, which may perform well on simple datasets of uncomplicated scenes, however, fail in complex datasets of scenes involving abundant objects, such as urban scenes. To tackle this problem, in this paper, we reasonably modify the previous problem setting and present a novel semantic-aware method. Specifically, in training, we use additional semantic label maps of training images, while in the test, no labels are required. We originally adopt a semantic knowledge distillation strategy to acquire semantic information from the labels and construct a particular normalization layer to introduce semantic information. Being aware of the pixel-level semantic information, our method can realize better I2I translation than the previous methods. Experiments are conducted on benchmark datasets of urban scenes to validate the effectiveness of our method.
Age Progression/Regression by Dual Discriminator Adversarial Autoencoder

The progression/regression of facial age can be applied to cross-age recognition or entertainment-related applications. It is challenging due to lack of facial expressions of the same person in a longer age range. Conditional Adversarial Autoencoder (CAAE) can learn facial manifolds and achieve smooth age development and regression at the same time. Since the generated face is different from the real face, we develop a novel model based on CAAE, which used two discriminators instead of one to solve this problem. The proposed model can produce better results and improve the similarity degree of age progression for different races of people.
PHNet: Parasite-Host Network for Video Crowd Counting

Crowd counting plays an increasingly important role in public security. Recently, many crowd counting methods for a single image have been proposed but few studies have focused on using temporal information from image sequences of videos to improve prediction performance. In the existing methods using videos for crowd estimation, temporal features and spatial features are modeled jointly for the prediction, which makes the model less efficient in extracting spatiotemporal features and difficult to improve the performance of predictions. In order to solve these problems, this paper proposes a Parasite-Host Network (PHNet) which is composed of Parasite branch and Host branch to extract temporal features and spatial features respectively. To specifically extract the transform features in the time domain, we propose a novel architecture termed as “Relational Extractor”(RE) which models the multiplicative interaction features of adjacent frames. In addition, the Host branch extracts the spatial features from a current frame which can be replaced with any model that uses a single image for the prediction. We conducted experiments by using our PHNet on four video crowd counting benchmarks: Venice, UCSD, FDST and CrowdFlow. Experimental results show that PHNet achieves superior performance on these four datasets to the state-of-the-art methods.
Digitization: Learnings from Ancient Disruptions, AI and the Digital Trio’s Functional Stage, and AI Superpowers Disrupting Us

“Digitization,” the buzzword of the 2010s, along with “disruption” characterizes a fundamental change to individuals and societies. Initially in this chapter, a little trust is built up by setting the current digital transformation into a historical context. Examples that show the potential and risks emerging from AI-based applications are discussed. Obviously, disruption is not new and key lessons can be learnt from the past. The “digital trio” (Robot Process Automation, blockchain, and quantum/supercomputing), i.e., digital technologies surrounding and contributing to AI, is explained for non-engineers and the performance is critically reflected. Using openly accessible AI platform Kaggle, a demonstration illustrates the output dependency on both key ingredients for AI: data and algorithms. Readers can immediately recheck calculation results themselves (without any software coding skills required): Based on a test dataset, different AI algorithms are used in an early warning system. It will be shown that even slight variations in the parametrization will lead to significantly different results. Obviously, this indicates a key problem of AI applications discussed as well in Part III, as misalignment on data and algorithms will ultimately lead to biased decisions and hence discrimination.The AI world emerged with an exponential pace. While there is a broad awareness of AI potential, for the last few years there is a clear duopoly on driving the AI agenda. The technologies used, the applications derived, and the impact on individuals’ and social lives will be illustrated by contrasting the two superpowers, the USA and China, in their approach to rule in the digital age. Despite the ethical consequences of the two very different endeavors, the USA with a so-called surveillance capitalism and China with the so-called market Leninism, there are massive implications not only on the social lives but as well on resource consumption, which provokes an exponential increase in energy demand (and with that realistically an increase in CO2) as well as costs.
Deep Learning on Active Sonar Data Using Bayesian Optimization for Hyperparameter Tuning

Sonar operations in littoral environments may be challenging due to an increased probability of false alarms. Machine learning can be used to train classifiers that are able to filter out most of the false alarms automatically, however, this is a time consuming process, with many hyperparameters that need to be tuned in order to yield useful results. In this paper, Bayesian optimization is used to search for good values for some of the hyperparameters, like topology and training parameters, resulting in performance superior to earlier trial-and-error based training. Additionally, we analyze some of the parameters involved in the Bayesian optimization, as well as the resulting hyperparameter values.
Research on optimization method for ordinal calculation of non-deterministic combinatorial coding

research-article Research on optimization method for ordinal calculation of non-deterministic combinatorial coding Share on Authors: Qi Zhao Heilongjiang University Heilongjiang UniversityView Profile , Jun Lu Heilongjiang University Heilongjiang UniversityView Profile , Xuanting Song Heilongjiang University Heilongjiang UniversityView Profile Authors Info & Claims CONF-CDS 2021: The 2nd International Conference on Computing and Data ScienceJanuary 2021 Article No.: 17Pages 1–5https://doi.org/10.1145/3448734.3450469Published:28 January 2021 0citation5DownloadsMetricsTotal Citations0Total Downloads5Last 12 Months5Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Thermal Object Detection Using Yolov3 and Spatial Pyramid Pooling

Thermal Imaging refers to capturing the infrared radiation of the images and creating images out of the infrared radiation. This infrared radiation can be captured by special purpose infrared cameras. These cameras do not have the need to capture the images in ambient light. The images can be captured in darkness and can be used to detect objects and living beings. Object detection techniques earlier used for normal images using deep learning can be extended to thermal images. The paper discusses the various Convolution Neural Networks(CNN) models available to detect the objects. We then look at the YOLOv3 and Spatial Pyramid Pooling(SPP) approach to detect objects in thermal images. YOLOv3 approach to detection is revolutionary as it uses a single CNN to get the bounding boxes and class probabilities for the image. The approach discussed here contains YOLOv3 and SPP layer. YOLOv3 contains a deep convolution neural network, the convolution neural network, we then add a Spatial Pyramid Pooling(SPP) layer on top of the CNN. The SPP layer removes the need for cropping the image in the fully connected layer. This gives a speedup of over 100 times. The precision and accuracy goals of over 80 percent are met with the above approach for the FLIR dataset.