Reviewerly/exHarmony huggingface dataset:
Knowledge Compensation Network with Divisible Feature Learning for Unsupervised Domain Adaptive Person Re-identification

Due to the large domain shift and the discriminative feature learning with unlabeled datasets, unsupervised domain adaptation (UDA) for person re-identification (re-ID) still remains a challenging task. Some current methods adopt a clustering-based strategy to assign pseudo labels to the unlabeled samples in target domains for classification. However, the rich knowledge of the model in different training stages is not fully utilized in those methods and the pseudo labels generated by clustering algorithms inevitably contain noise, which would limit the performance of re-ID models. To tackle this problem, a Knowledge Compensation Network with Divisible feature learning (KCND) is proposed in this paper, which aggregates the past-to-present knowledge of models from training samples for discriminative feature learning and resists the label noise produced by clustering. Also, a novel compensation-guided softened loss is developed to enhance the generalization and robustness of re-ID models. Our experimental results on large-scale datasets (Market-1501, DukeMTMC-reID and MSMT17) have demonstrated the performance of KCND is better than other methods in terms of the mAP and CMC accuracy.
Vibration insensitive single-shot interferometry using a wide-field laser microscope

Here, we report measurements made with an external vibration-insensitive single-shot interferometer, which is applicable for on-machine measurement. Previously, we developed a simple single-shot interferometry method in which the optical intensities of interference fringes appear not as sinusoidal distributions but have a sawtooth-like distribution, and the peak distortion direction depicts the slope of the measured surfaces. These properties allow use of this method in vibration-insensitive interferometry. In this study, we have experimentally confirmed this concept through measurement of a quarter of a Si wafer specimen 50 mm in diameter. The vibration source was a motorized linear stage the surface of which fluctuated between ±400 nm in height. The obtained surface profiles of the Si wafer were consistent with measurements made with a commercial interferometer within 30 nm. • A novel vibration-resistant optical interferometry method was achieved. • Metal was deposited on one surface of a glass reference plate. • Optical intensity of interference fringe has a sawtooth-like distribution. • A single interferogram is sufficient for determination of surface inclination. • Measured profiles were coincident with target values within 30 nm.
Layout Structure Assisted Indoor Image Generation

The existing methods can generate images in accord with scene graph, but the obtained images may appear blurs at the edges and disorders in the structure, due to the lacks of the structure information. In this paper, by considering the indoor images contain more layout structures than outdoor ones, we focus on the indoor image generation assisted with the layout structures. In the proposed method, through fusing the scene graph features together with the layout structure, the graph convolutional network is employed to convert the fused semantic information into the feature representation of scenes. Subsequently, a refined encoder-decoder network is also used for generating the final images. In the experiments, we compare the proposed method with the existing works on the indoor image dataset in terms of subjective and objective evaluations. The experimental results show that our method can achieve better IoU metric, and the visualized results also illustrate that the proposed approach can generate more clear indoor images with better layout structures.
DarkNet and Hidden Services

The continuous growth of the Internet on a large scale flourishes technology that heavily impacts the computing world. Cyberspace is already an attractive area on the Internet where technologies are threatened with a wide area of attack vectors, directing the attackers to weaken the systems. We are seamlessly using the Internet and its services to access a large amount of indispensable information, work from home, entertainment, and even online shopping. Internet is currently a central hub for vast information gathered from all around. It is an integral part of our daily lives as if without the Internet, our current activities will become unimaginable. Statista, a renowned database company, recently blogged about the active Internet users being over 4.33 billion people, which confined 56% of the global population. India, China, and the USA are the top countries among others in using the Internet. However, the services Internet is providing are limited and constrained. The information you are accessing is fetching from the normal web, which is easily accessible and known as the “surface web”. The information and the services we browse each day from Google, Yahoo, and Bing are a small percentage of the Internet. Still, below the surface, the Internet is further partitioned into something called “Deep Web” and “DarkNet”. Many experts remarked that surface Internet is comprised of only 4%, and the rest of the large portion is further divided as 90% to Deep Web and 6% to DarkNet. Furthermore, the Deep Web consists of content such as financial records, academic databases, government resources, legal documents, scientific reports, and many more. These cannot be accessed nor found directly using the surface web. Hence, the user will need to use advanced search tools to find such services. In contrast to Deep Web, DarkNet, which is the favorite spot of all, as it is an anonymous network where you can hide your privacy, offers completely hidden services. The DarkNet services include illegal information, drug trafficking, private communication, weapon dealers, hackers, and many more. You need to use special tools and software such as TOR, I2P, or Freenet to access such services. This chapter comprises a brief introduction of the Internet and its services. We also elaborate surface web, Deep Web, and DarkNet and its hidden services. This chapter aims to provide a generalized idea on hidden services of DarkNet and how it operates in manipulating your privacy and confidentiality. Finally, we will summarize with an example of providing a hidden service on the TOR network, along with some good practices for maintaining your anonymity and privacy.
Modular Reinforcement Learning Framework for Learners and Educators

Reinforcement learning algorithms have been applied to many research areas in gaming and non-gaming applications. However, in gaming artificial intelligence (AI), existing reinforcement learning tools are aimed at experienced developers and not readily accessible to learners. The unpredictable nature of online learning is also a barrier for casual users. This paper proposes the EasyGameRL framework, a novel approach to the education of reinforcement learning in games using modular visual design patterns. The EasyGameRL framework and its software implementation in Unreal Engine are modular, reusable, and applicable to multiple game scenarios. The pattern-based approach allows users to effectively utilize reinforcement learning in their games and visualize the components of the process. This would be helpful to AI learners, educators, designers and casual users alike.
Face detection in a real-time videostream on Raspberry Pi

This paper is focused on the topic of the system of face detection in a real-time videostream on a low-power device. It describes the implementation and differences of two selected approaches, specifically the use of CNN or Haar classification. It explains the importance of the data preparation, especially the process of modifying the input images. Also, it deals with the problems and limitations arising mainly due to the choice of using a low-power Raspberry Pi device.
Robust Automatic Clustering Based on Local Density with Glowworm Swarm Optimization

Robust clustering based on nature inspired algorithms have gained a lot of attention due to their ability to effectively find solutions for data clustering problems while detecting and eliminating outliers. This paper proposes a robust automatic clustering algorithm based on Glowworm Swarm Optimization (GSO). The GSO is a meta-heuristic algorithm inspired from the ability of glowworms to change their luminescence and thus to glow at different intensities based on their capability (which represents fitness value). The proposed algorithm, termed as Robust Glowworm Swarm Clustering (RGSC), is capable of capturing multiple local maxima of the fitness function, which are representing cluster centers. The proposed algorithm has been assessed on six artificial as well as eight real datasets having number of clusters varying from 2 to 10. The superior performance is reported in terms of evaluation metrics like Silhouette Score and Adjusted Rand Index, as compared to existing clustering algorithms K-means, Mean-shift, DBSCAN, and Robust PSO.
Generating of Plasma Discharge Video by Generative Adversarial Network

During the experiment of nuclear fusion, a lot of videos containing plasma discharge are recorded inside Large Helical Device. An observation of the recorded videos of plasma light emission can lead to a new discovery or help to optimize the operation parameter of the experiment. An unusual plasma discharge which may cause a damage on the device, is expected to be foreseen through a prediction method. Due to the shortage of videos having such unusual emission, it is required to generate more videos having similar phenomenon. However, video generation is very challenging as the generated video should have not only similarity in features with the real one but also a plausibility in frame-by-frame transition, especially in the case of plasma discharge videos. Thus, this paper proposes a method to generate a plasma emission video by using Generative Adversarial Network (GAN). It has been confirmed that the proposed generative model can produce a new video having plasma emission phenomenon with a very smooth changing of frames.
Application of Neural Network Algorithm in Robot Eye-Hand System

With the advent of the intelligent era, intelligent industrial robots integrated with vision systems have penetrated into many fields. Especially robots with hand-eye functions play an important role in the fields of automobile manufacturing, logistics, and aviation exploration. Radial basis function (RBF) neural network has a high degree of non-linear mapping ability. This paper analyzes the structure characteristics, learning algorithm and application of RBF neural network in the robot eye-hand system, and analyzes the non-linearity of RBF neural network. The linear approximation characteristics are theoretically verified. The purpose of this article is to study the application of neural network algorithms in robot eye-hand systems. Simulation experiments show that the designed algorithm can track the ideal position well, and can significantly reduce chattering, and improve the control performance of the manipulator system when the system itself has uncertainty and external interference.
Analysis of the Influence of Computer Software Modeling Technology in Modeling Teaching

The rise and popularization of new computer modeling software technology has not only been widely used in the field of industrial design, but also occupies an important position in the fields of architecture, advertising, film, and education, and more and more teachers use this technology as an auxiliary tool for modeling teaching in various disciplines has had an important impact on modern and intelligent teaching activities. This article focuses on the analysis of the influence of computer software modeling technology in modeling teaching. This article first analyzes the application of computer software modeling technology in each link of modeling teaching, and expounds from the aspects of computer software modeling technology intervention in the problem characterization link and the establishment of the model link. This paper uses questionnaire survey and interview methods to investigate the current state of modeling teaching in our country. Taking mathematics students in H colleges as an example, starting from the student level and teacher teaching level, it explores the problems and reasons in the modeling teaching process. According to the questionnaire survey of “Do you think the knowledge learned in the modeling course is helpful in life?”, the data is sorted out to Table 1; 22.88% think “useful, but the study is not deep enough”; 22.88% think “useful” 33.83% of those who think they are “useful, but have not learned yet” accounted for 41.79% of those who thought “useful, only learned but can’t use it”. It shows that in the current modeling teaching process, one of the common problems of most students is the weak abstract ability of mathematics, and the inability to accurately describe actual problems in mathematical language.
Performance Evaluation of Graph Partitioning on Many-core System

Max-flow or min-cut is one of the important strategies for modelling and addressing practical problems in computer vision, image processing, and optimization theory using graph algorithms. It's been extensively used in a variety of applications, including image segmentation, flow network. As graph can be large and complex in terms of the amount of nodes/edges and the connection between them, the graph partitioning technique helps to divide the graph into sub-parts by using max-flow and min-cut method. According to the max-flow min-cut, the total weight of the edges in a flow network's minimal cut equals the maximum quantity of flow travelling from the source to the sink. This paper describes both serial and parallel implementations of the push-relabel approach for max-flow and min-cut. The parallel implementation on many-core system (GPGPU) shows better speedup than serial implementation of the prooosed algorithm.
Game Mechanic Alignment Theory

We present a new concept called Game Mechanic Alignment theory as a way to organize game mechanics through the lens of systemic rewards and agential motivations. By disentangling player and systemic influences, mechanics may be better identified for use in an automated tutorial generation system, which could tailor tutorials for a particular playstyle or player. Within, we apply this theory to several well-known games to demonstrate how designers can benefit from it, we describe a methodology for how to estimate “mechanic alignment”, and we apply this methodology on multiple games in the GVGAI framework. We discuss how effectively this estimation captures agential motivations and systemic rewards and how our theory could be used as an alternative way to find mechanics for tutorial generation.
Mitigating Deep Double Descent by Concatenating Inputs

The double descent curve is one of the most intriguing properties of deep neural networks. It contrasts the classical bias-variance curve with the behavior of modern neural networks, occurring where the number of samples nears the number of parameters. In this work, we explore the connection between the double descent phenomena and the number of samples in the deep neural network setting. In particular, we propose a construction which augments the existing dataset by artificially increasing the number of samples. This construction empirically mitigates the double descent curve in this setting. We reproduce existing work on deep double descent, and observe a smooth descent into the overparameterized region for our construction. This occurs both with respect to the model size, and with respect to the number epochs.
Computing the Relevance Degree of Social Media Network

Social Media Networks (SMNs) became very important in our life. SMNs made people communicating with almost everyone in the world. This technology facilitates the sharing of ideas and information through virtual communities. Accounts (pages) on SMNs are public profiles which are created for different reasons such as businesses, brands, and celebrities. These accounts can gain unlimited number of fans based on a profile orientation and its description that is published by owners. Accordingly, SMNs users face the content-related problem which is clear when they found unrelated content with profile orientation. A possible solution is computing the degree of relevance between the orientation and profile account content. This can be achieved by using the term frequency-inverse document frequency (TF-IDF) algorithm with some algebraic geometry such as intersection sets and mathematical framework such as probability. The promised output of this study is to obtain a matching percentage between the profile orientation and the content. Such outcomes can help users on social media networks make a suitable decision whether like or follow a certain account based on the content related.
Analysis of voxel-based 3D object detection methods efficiency for real-time embedded systems

Real-time detection of objects in the 3D scene is one of the tasks an autonomous agent needs to perform for understanding its surroundings. While recent Deep Learning-based solutions achieve satisfactory performance, their high computational cost renders their application in real-life settings in which computations need to be performed on embedded platforms intractable. In this paper, we analyze the efficiency of two popular voxel-based 3D object detection methods providing a good compromise between high performance and speed based on two aspects, their ability to detect objects located at large distances from the agent and their ability to operate in real time on embedded platforms equipped with high-performance GPUs. Our experiments show that these methods mostly fail to detect distant small objects due to the sparsity of the input point clouds at large distances. Moreover, models trained on near objects achieve similar or better performance compared to those trained on all objects in the scene. This means that the models learn object appearance representations mostly from near objects. Our findings suggest that a considerable part of the computations of existing methods is focused on locations of the scene that do not contribute with successful detection. This means that the methods can achieve a speed-up of 40–60% by restricting operation to near objects while not sacrificing much in performance.
Simulation Tool for the Point Placement with Maximal Coverage

In recent years, point location problems arise in many areas such as sensor location, crowd and transport simulations, urban planning, visibility analysis, light sources placement. The problem is to define a arbitrary set of point, which covers specific areas or points on a plane or a depth map. One of the main problems is the optimal location estimation of points, which are placed on the grids with huge resolution. The work provides an algorithm for the point placement on a plane based on the search of areas with maximal number of overlapping circles of given radius. Proposed approach is implemented as a program utility by Python programming language.
Malaria Parasite Detection using Residual Attention U-Net

Malaria is a life-threatening disease caused by Plasmodium parasites, and which is still a serious health concern worldwide nowadays. However, it is curable if early diagnosis could be performed. Due to the lack of access to expertise for diagnosis of the disease, often in poorly developed and remote areas, an automated yet accurate diagnostic solution is sought. In Malaysia, there exists 5 types of malaria parasites. As an initial proof of concept, automated segmentation of one of the types, Plasmodium falciparum, on thin blood smear was experimented using our proposed Residual Attention U-net, a type of Convolutional Neural Network that is used in deep learning system. Results showed an accuracy of 0.9687 and precision of 0.9691 when the trained system was used on verified test data.
Let it Bee: A Case Study of Applying Triadic Game Design for Designing Virtual Reality Training Games for Beekeepers

The Triadic Game Design (TGD) framework is a way to structure the design process of serious games along the worlds of reality, meaning, and play. We used TGD to undertake the first steps in designing serious virtual reality games for training of beekeepers. Beekeeping is a demanding activity as it requires both theoretical knowledge and physically demanding practice. Current educational offers can only rely on “traditional” media such as texts, images, and videos. Training with actual bee hives would be desirable but is difficult to provide, for a variety reasons (e.g., bees being very sensitive organisms). After an initial assessment of the current state of things, we developed a simple VR prototype, where players had to find a queen inside a hive. We then used this prototype to conduct a user study with beekeepers of different competency levels and elicited information about beekeeping in general, the specific game experience, and how such a game could help in the future of beekeeper training. We identified seven learning modes and developed three of them further into game concepts that focus on different aspects. We reflect on how Triadic Game Design supported our design process and provide a hands-on overview of seven steps that future designers can follow in similar circumstances.
Features of Conducting an Independent Assessment of the Quality of the Conditions for the Implementation of Educational Activities by Educational Institutions of Higher Education in 2020

The article highlights the key features of conducting in 2020, commissioned by the Ministry of Science and Higher Education of the Russian Federation, a study on an independent assessment of the quality of conditions for the implementation of educational activities by higher education organizations. The main results of the study are presented.
Automobile insurance fraud detection

The risk of incurring financial losses from fraudulent claims is an issue concerning all insurance companies. The detection of such claims is not an easy task. Moreover, a number of old-school methods have proven to be inefficient. Statistical techniques for predictive modelling have been applied to detect fraudulent claims. In this article, we compare two techniques: Artificial neural networks and the Naïve Bayes classifier. The theory underpinning both techniques is discussed and an application of these techniques to a dataset of labelled automobile insurance claims is then presented. Fraudulent claims only constitute a small percentage of the total number of claims. As a result, datasets tend to be unbalanced. This in turn causes a number of problems. To overcome such issues, techniques which deal with unbalanced datasets are also discussed. The suitability of Neural Networks and the Naïve Bayes classifier to the dataset is discussed and the results are compared and contrasted by using a number of performance measures including ROC curves, Accuracy, AUC, Precision, and Sensitivity. Both classification techniques gave comparable results with the Neural network giving slightly better results than the Naïve Bayes classifier on the training dataset. However, when applied to the test data, the Naïve Bayes classifier slightly outperformed the artificial neural network.