Reviewerly/exHarmony huggingface dataset:
Validating DLO Models from Shape Observation

In this paper, the problem of fitting the model of deformable linear objects from the observation of the shape under the effect of known external forces like gravity is taken into account. The model of the deformable linear object is based on dynamic splines, allowing to obtain a reliable prediction of the object behavior while preserving a suitable efficiency and simplicity of the model. The object shape is measured by means of a calibrated vision system, and a fitting between the observed shape and the theoretical model is defined for validation. Experiments are executed in different conditions, showing the reliability of the proposed spline-based model.
Diversifying Relevant Search Results from Social Media using Community Contributed Images

Availability of affordable image and video capturing devices as well as rapid development of social networking and content sharing websites has led to the creation of new type of content, Social Media. Any system serving the end user’s query search request should not only take the relevant images into consideration but they also need to be divergent for a well-rounded description of a query. The previous state-of-the-art methods used a number of views of a particular image as one of the key parameters in order to achieve diverse results. This parameter, while improving the overall results, can omit images that are most recently taken. This might not work if the user is interested in recent images when solving the problem of divergence. Secondly, all prior work considered only one of the clustering algorithms for diversification. The performance of most of these clustering techniques is highly data-dependent and might render inefficient for different kinds of datasets. The main focus of this paper is to use visual description of a landmark location by choosing diverse pictures that best describe all the details of a queried location from community-contributed data sets. For this, an end-to-end framework has been built, to retrieve relevant results that are also diverse. Different retrieval re-ranking and diversification strategies are evaluated to find a balance between relevance and diversification. Clustering techniques are employed to improve divergence. A unique fusion approach has been adopted to overcome the dilemma of selecting an appropriate clustering technique and the corresponding parameters, given a dataset to be investigated. Extensive experiments have been conducted on the Flickr Div150Cred dataset. This system has proved to achieve results that are on par with the start-of-art work done on the MediaEval Challenge. This is achieved without using one of the key parameters that contribute to the improved overall metric results - "Number of Views".
Analysis of TV Advertising Video Color Using Color Extraction Based on K-means Clustering using Big Data

Color is an emotional element that moves people's minds. Advertising stimulates people 's emotions, raising the value of goods and promoting consumers' desire to purchase. The color strategy in advertising has become an important element of marketing. In today's competitive environment, color marketing is used as a key management strategy that differentiates itself from other products and determines the success or failure of the company. Based on the Big Data collected by Creative Advertising Center of Korea Advertising Association, this paper analyzed to find elements other than the color marketing strategy used in the existing advertisements. In this paper, we propose a new method to collect color information using K-means clustering based on the advertisement image collected by web crawling, and analyzed the correlation between the consumers ‘involvement’ and the color of the advertisement according to the advertisement category.
Outback Steakhouse testing camera system to track guests' experience

Outback is going to utilize video surveillance to enhance their internal analytics. One industry analyst says it will become the norm.
Application of ground magnetic and soil geochemical surveys for mapping potential kimberlite bodies in the southern margin of the Archaean Kaapvaal Craton, South Africa

In this study, the southern margin of the Archean Kaapvaal Craton in South Africa is selected to investigate the occurrences of potential kimberlite bodies. Ground magnetic survey was conducted to identify potential targets for further exploration. Euler 3D deconvolution and Keating correlation coefficients are the processing techniques that were applied to the ground magnetic data to identify geological features that correspond to kimberlite models. These techniques revealed several potential targets that resemble geologic models of kimberlite bodies. Seven of these targets (Targets A, B, C, D, E, F and G), spatially cluster along or at the intersections of linear structures, which is consistent with one of the primary geological conditions that control the emplacement of kimberlite bodies. A follow-up soil sampling and analysis was conducted to assess the geochemistry of soils at the seven targets. The major element geochemistry of soil samples suggest that Target B possesses residual soils that originated from kimberlite intrusion, while the remaining six samples show geochemical characteristics of soils derived from crustal rocks. The composition of compatible (Ni, Cr, V, Co, Cu and Zn) and incompatible (Nb, Zr, Sr, Ce, and Ba) trace elements of Target B shows similarity with the known Group 1 Kimberlites locally and globally, thus confirming that Target B most probably belongs to Group 1 Kimberlite petrological clan. This, however, does not preclude the possibility of modification of kimberlite chemistry by crustal assimilation during ascent and post-emplacement processes such as weathering as demonstrated by elevated concentrations of SiO 2 , Al 2 O 3 and Rb and low content of MgO. In light of this, Target B may be interpreted as a potentially contaminated and weathered Group 1 Kimberlite pipe which intruded the Karoo Supergroup at the intersection of linear structures. This finding is consistent with the presence of large number of known diamondiferous kimberlites in this region, which post-date the formation of the Karoo Supergroup. • Ground magnetic data is processed to locate kimberlite potential targets. • A follow-up soil sampling is conducted at seven located targets. • Samples are analysed using X-Ray Fluorescence technique. • The results suggest that one target is of mantle derivation where kimberlites originate. • This target shows geochemical similarity with Group 1 Kimberlites.
Enabling Privacy-Preserving Rule Mining in Decentralized Social Networks

Decentralized online social networks enhance users’ privacy by empowering them to control their data. However, these networks mostly lack for practical solutions for building recommender systems in a privacy-preserving manner that help to improve the network’s services. Association rule mining is one of the basic building blocks for many recommender systems. In this paper, we propose an efficient approach enabling rule mining on distributed data. We leverage the Metropolis-Hasting random walk sampling and distributed FP-Growth mining algorithm to maintain the users’ privacy. We evaluate our approach on three real-world datasets. Results reveal that the approach achieves high average precision scores () for as low as 1% sample size in well-connected social networks with remarkable reduction in communication and computational costs.
Optimal Strategy Synthesis for Stochastic Boolean Games

Abstract In this study, we regard a classification of winning strategy and payoff function over infinite plays. The central goal is to examine the values of the game and then to determine the existence of optimal (∈-optimal) strategy. Furthermore, we are interested in the subject of what sort of optimal (∈-optimal) strategy exists. We first review on generalised reachability games and then we introduce a new game, called Boolean games and concentrate on games with a Boolean combination of the reachability games. The primary contribution is on the existence of ∈-optimal finite memory strategy of each player for any ∈ &gt; 0. We also prove every player has no ∈-optimal memoryless strategy for some Boolean game.
Design and Build 3d Educational Game "Motorized Millennials" for Indonesian Teenagers

Traffic signs are one of the things road equipment which consists of symbols, numbers, letters, sentences or a combination of them serves for prohibitions, orders, warnings, and instructions for road users. The younger generation often neglects even many who do not obey the traffic rules, as a result, accidents often occur. From a research Rahmi concluded that 84.5% of students ride motorbikes to school and get 56.8% respondents and have criteria for less safe actions in two-wheeled driving and 43.3% safe criteria including driving while communicating with a mobile phone 51% (Rahmi, 2009 ). An innovation that is suitable for Indonesian youth in studying traffic signs is the game method. Because teenagers tend to like to play and learn. so the game method with game media is very suitable to be applied to make it easier for teenagers to learn about traffic signs. Therefore, the purpose of this research is one of them can attract the interest of the younger generation to be able to learn to recognize traffic signs in order to reduce accidents while driving. The data collection methods that we use in this study include: Interviews, Field Observations, Functional Requirements Analysis, System Design, System Testing, Application Testing Design. We have carried out the results of testing this educational game starting from testing all buttons, displaying questions, displaying scores, and displaying every available menu. The results of research that have been carried out are displaying menus in motorized millennial games including: first we install the apk, bring up the application icon, display the game start page starting from: how to play, start and exit.
Machine Learning Derived Entanglement Witnesses

We demonstrate a correspondence between linear support vector machines (SVM) and entanglement witnesses, and use this correspondence to generate entanglement witnesses for bipartite qubit, tripartite qubit, and bipartite qutrit systems.
Stroke Order Writing Ability of Non-Native Speakers Based on Statistical Analysis

Chinese character writing has always been the primary difficulty for non-native speakers. How to make foreign students write Chinese characters accurately and form good writing habits is an urgent problem to be solved in teaching. In this paper we randomly selected 30 students with intermediate level from Korea and Burundi studying Chinese in Jinzhou as our participants, and we hope this would be helpful for the character study on big data. We conducted two questionnaires to get the data on how the participants master character writing and how they write stroke order, and then investigated whether there was significant difference between the two groups for each questionnaire. We found that for the character writing there wasn't significant difference. However, there was significant difference for stroke order writing between the two groups. Then we analyzed the reasons for the diversity of students’ writing orders, and attempted to put forward solutions, so as to promote the development of Chinese character teaching.
Competitive physical interaction by reinforcement learning agents using intention estimation

The physical human–robot interaction (pHRI) research field is expected to contribute to competitive and cooperative human–robot tasks that involve force interactions. However, compared with human–human interactions, current pHRI approaches lack tactical considerations. Current approaches do not estimate intentions from human behavior and do not select policies that are appropriate for the opponent’s changing policy. For this reason, we propose a reinforcement learning model that estimates the opponent’s changing policy using time-series observations and expresses the agent’s policy in a common latent space, referring to descriptions of tactics in open-skill sports. We verify the performance of the reinforcement learning agent using two novel physical and competitive environments, push-hand game and air-hockey. From this, we confirm that the latent space works properly for policy information because each latent variable that represents the machine agent’s own policy and that of the opponent affects the behavior of the agent. Two latent variables can clearly express how the agent estimates the opponent’s policy and decides its own policy.
Ways to counteract transnational crime in Russia

The article is devoted to possible ways of counteracting transnational organized crime in Russia. A number of proposals have been developed to change and toughen the measures to combat transnational crime in Russia. The current situation of transnational criminal groups in the world is analyzed, and the threat they pose is demonstrated. The importance of strengthening international cooperation for effective work in this direction is noted. The current mechanism of counteraction in foreign countries is considered. The materials of the article can be used as teaching materials for courses related to economic security.
CU-Net+: Deep Fully Interpretable Network for Multi-Modal Image Restoration

The network interpretability is critical in computer vision related tasks, especially for tasks involving multiple modalities. For multi-modal image restoration, one recent method, CU-Net, introduces an interpretable network based on a multi-modal convolutional sparse coding model. However, its network architecture does not mimic in full the proposed sparse model. In this paper, we overcome the limitation of CU-Net by using recurrent scheme, and this leads to a fully interpretable network which we call CU-Net+. In addition, we relax the constraint on the number of common and unique features in CU-Net, for making it more consistent with real condition. The effectiveness of the proposed CU-Net+ is evaluated on RGB guided depth image super-resolution and flash guided non-flash image denoising tasks. The numerical results show that CU-Net+ outperforms other interpretable or non-interpretable methods, with 0.16 RMSE and 0.66 dB PSNR improvement over CU-Net for the two mentioned tasks, respectively. Code is available at https://git;hub.com/JingyiXu404/CU-Net;-plus.
Contactless Interface for Navigation in Medical Imaging Systems

Medical informatics in planning the surgeries is rapidly changing lately with new systems and approaches and one of the latest is contactless approach to visualization systems used during the surgeries. This approach is based on contactless together with adopting algorithms from augmented and virtual reality, enabling surgeons in the operating room more freedom in touchless human-computer interaction in analyzing and visualization of complex medical data. We are proposing the novel contactless plug-in interface for the DICOM viewer platform using a camera controller that tracks hand/finger motions, with no hand contact, touching, or voice navigation. Our proposal flow is oriented to solve several issues we faced during the proof-of-the-concept of previous contactless surgery approach while using the Leap Motion as a tracking camera, both with software and hardware for spatial interaction. In this paper, we will focus on increasing the user experience in 3D-virtual rendered space and proposing the solution that could be used as a benchmark for 3D virtual navigation which integrates high-resolution stereo depth camera with medical imaging systems in order to obtain contactless ‘in the air’ real control of medical imaging systems with surgeon’s hands.
Contrastive Loss On Masked Face Verification

The outbreak of COVID-19 has encouraged people to wear their masks more frequently than ever. However, the absence of much facial information from the masked face will cause failures in many current face recognition and verification functions to recognize the individual’s identity. To tackle this problem precisely, our group builds a modified SimCLR model with the contrastive loss that is able to extract similarity features from individuals regardless of whether a mask is worn. From our experiments, we find out that our usage of contrastive loss leads to a large improvement in the testing verification accuracy compared to a baseline model with the commonly used MSE loss.
Study on the Application of Computer Network Resources in Japanese Teaching

Abstract With the popularization of Internet technology, network education has become an important tool in various industries, especially in English, Japanese and other language teaching. Through the network database, we can get rich and systematic Network Resources (hereinafter referred to as NR), which can help students learn Japanese better. With the modernization of education, Japanese teaching (hereinafter referred to as JT) in Colleges has been developing in a diversified direction, which will gradually adapt to the needs of the times. Japanese is a very difficult foreign language to learn, which requires us to carry out a variety of teaching. Through multimedia NR, we can give full play to the role of students’ learning subject. By stimulating students’ interest in learning, students can actively participate in Japanese learning and practice in the Japanese environment. Through the NR, students can get the learning resources they need, which will help students learn Japanese better. This paper analyzes the importance of NR in JT.
An Image Watermarking Technique Proposed Based on Discrete Cosine Transformation and Pseudo-Random Generator

The proposed technique to embed an image watermark in a host image depends on the discrete cosine transform and the Geffe algorithm in this search. The proposed algorithm uses the Geffe algorithm for encryption of the watermark. Discrete cosine transform (DCT) coefficients are then computed by dividing the host image into non-overlapped 8x8 blocks. The performance evaluation of the suggested technique was conducted using the peak signal-to-noise ratio for watermark 64x64 and 512x512 host images: Lena, Cameraman, and Pepper images give similar results. This technique does not require a host image in the watermark extraction process, then also watermark is extracted completely and better than its counterparts available
Classifying Metastatic Cancer Lymph Node Histopathology Using Cross Validation on Convolutional Neural Networks

Cancer is a group of diseases involving abnormal cell growth with the potential to invade or spread to other parts of the body. Breast cancer is the most prevalent cancer among women worldwide and histopathological evaluation of lymph node and whole-slide images (WSIs) play an important role in detection and curing. In this paper, we propose a method to automatically predict pN-stage for whole slide image by leveraging multi-scale features and path aggregation. We used cross-validation on Convolutional Neural Networks (CNN) architecture with fast ai densenet [1] (AUC>0.975) for slide level detection of tumor cells.
ByteDance reaches $92 million settlement with U.S. TikTok users over data privacy concerns

Under the proposed terms of the agreement, TikTok will no longer collect or store a user's biometric information, including facial characteristics, or track users' GPS data. The company will also be prohibited from storing U.S. user data in databases outside of the country.
Evaluation of virtual laboratory media to learning geometry in Universitas Terbuka

The virtual laboratory is developing rapidly at present. The increasing numbers ofstudents of state or private universities who eagerly find references related topractical geometry courses online is one of the development indicators. Theinvention of virtual laboratories with Virtual Reality based become the entity ofconcern to add values to lectures. This study applied ADDIE, a R&D model(Analysis, Design, Develop, Implementation and Evaluation). The steps donewere implementing and marketing the product in State and Private Universities inthe region of Central Java and its surroundings. Based on the findings on the useof virtual geometry lab product, it scores 91 and 94 meaning that the virtualgeometry lab product is very suitable to use in teaching. Then more than 90% oflecturers and students at Universitas Terbuka are pleased to use it. Enabling thevirtual laboratories with Virtual Reality based, enhanced the teaching andlearning process.
(λ, μ)-Intuitionistic fuzzy filters on Heyting-Algebras

By combining intuitionistic fuzzy filter with heyting-algebras, the definitions of intuitionistic fuzzy filter, (λ, μ)-intuitionistic fuzzy filter and (λ, μ)-intuitionistic fuzzy lattice filter are given. The properties of the defined filter are discussed. It is concluded that (λ, μ)-intuitionistic fuzzy filter has intersection invariance. Finally, the equivalence between (λ, μ)-intuitionistic fuzzy filter and (λ, μ)-intuitionistic fuzzy lattice filter is proved.
Multi-cipher Encrypter Using Symmetric Key Algorithms

Data confidentiality is one of the primary concerns for all organizations as well as individuals all around the world. With the advent of technology and easier access of attacking tools and algorithms on the internet, the last decade saw an exponential rise in the number of attackers, working independently as well as in structured organizations. These attackers aim to steal information, primarily for monetary gains, which affects millions of people and organizations, causing great financial losses on a regular basis. Data confidentiality gets vital importance for all, as information on shared networks are susceptible to such attacks. Protecting confidential information from unauthorized access through encryption and ciphering is one such measure which helps maintain confidentiality. Since no encryption technique is fully secure and often breaks on brute force attacks, applying multiple encryptions increases the security by an enormous factor. This research paper explores the application of one such multiple encryption technique for securing data by applying simple symmetric key cipher algorithms. In this paper, we propose the implementation of a custom text encrypter using simple algorithms such as Caesar Cipher, Hill Cipher and Base Change Cipher to convert textual data into a secure cipher according to the user’s selection of layers, keys and ciphering techniques. This naive approach thus explores the methods which can be utilized for strengthening ciphers and ensures data confidentiality by providing an easy to implement user-friendly interface.
Curvelet Based Texture Features for Breast Cancer Classifications

Abstract One of the sources of death among women is breast cancer. It is well known that Mammogram is the best method for breast cancer detection. Subsequently, there are solid requirements for the improvement of computer aided diagnosis (CAD) systems to assist radiologists in making decision. In this paper, the curvelet changes is proposed to classify the breast cancer. Curvelet refers to multi-level change which have the characteristics of directionality and anisotropy. It splits several characteristic impediments of wavelet to edges of an image. Two component extraction techniques were created associated with curvelet and wavelet coefficients to separate among various classes of breast. Finally, the K-Nearest Neighbor (KNN) classifiers were utilized to decide if the district is unusual or ordinary. The adequacy of the suggested strategies has been implemented with Mammographic Image Analysis Society (MIAS) data images. All the dataset is utilized by the suggested strategies. Then calculations have been applied with both curvelet and wavelet for correlation test were performed. The general outcomes show that curvelet change shows superior compared to the wavelet and the thing that matters is measurably noteworthy.