Reviewerly/exHarmony huggingface dataset:
Disentangled Representations by Pseudo-Maximum Mutual Information for Interpreting Multi-Layered Neural Networks

The present paper aims to propose a new type of information-theoretic method to disentangle complex information to have easily interpretable representations for multi-layered neural networks. By this disentanglement of complex information, multi-layered neural networks can be easily compressed to the simplest ones with simple, linear and individual relations between inputs and outputs. The principal idea is to train neural networks by supposing maximum mutual information states before learning, namely, pseudo-maximum information maximization. This pseudo-maximum information method can greatly facilitate the implementation of maximum information procedures. The method was applied to the well-known Boston housing data set for easily reproducing the present results. The experimental results confirmed that pseudo-mutual information can be used to increase actual mutual information. In addition, when mutual information increased, compressed weights from multi-layered neural networks became similar to the correlation coefficients between inputs and targets of original data set. Thus, the method could successfully show that the main inference mechanism can be based on linear and individual relations between inputs and outputs with additional and peripheral nonlinear relations.
Dual OS Smart Mirror Using Raspberry Pi (Allure Mirror)

This paper describes the designing and implementation of a dual operating system wall mirror, called “Allure Mirror”. It is a device that can act as a mirror as well as a dual -OS immersive display which can display time, date, weather, news, can play video (via. YouTube, Prime Video, Netflix, etc.), can do video calls, use social media, check mails, can help in fitness and many more. Voice instructions can be used to communicate with it. The Allure Mirror includes features such as real-time data and information alerts, voice orders, content viewing via LCD display, microphone, and webcam, and more. Voice instructions can be used to communicate with the Allure mirror.
[DC] Embodying an avatar with an asymmetrical lower body to modulate the dynamic characteristics of gait initiation

Virtual reality (VR) enables the user to perceive body owner ship towards a virtual body. This illusion is induced through first-person perspective (1PP) and synchronous movement with the real body. Previous studies have shown that pronounced differences between the real and the virtual body lead to changes in the user's behavior. It has also been shown that modifying the body image can affect the user's movements. Nevertheless, the state of the art does not refer to the kinetic and kinematic impacts of one virtual lower limb deformation. Therefore, this paper presents a methodology exploring the impact of a self-avatar with an asymmetrical lower body (one limb longer or larger than the other) on the dynamic characteristics of the user during a gait initiation task.
Research on stealth route planning based on improved RRT algorithm

Aiming at the problem of stealth penetration route planning, this article establishes the constraints of route planning firstly, including the maximum turning angle and climbing angle. The objective function is established with the constraints of navigation and radar detection probability. Then the improved RRT algorithm is used to search for way points in 3D space and the route search model can be established. Under the given condition, the simulation analysis is carried out to verify the feasibility to the model.
Does shortening the release cycle affect refactoring activities: A case study of the JDT Core, Platform SWT, and UI projects

Several large-scale companies such as Google and Netflix chose to adopt short release cycles (e.g., rapid releases) in recent years. Although this allows these companies to provide updates and features faster for their users, it also causes developers to have less time to dedicate to development activities other than feature development. In this paper, we investigate how refactoring activities were impacted by the adoption of shorter releases. We extract all refactorings applied over a period of two years during traditional yearly releases and almost two years during shorter quarterly releases in three Eclipse projects. We then analyze both time periods’ refactoring activities to understand how refactoring activities can be impacted by shortening the release cycles. We observe reduced refactoring activities in one project and a decrease in more complex refactoring operations after shortening the release cycles. We also find that weekly efforts dedicated to refactoring activities was lower across all projects after shortening the release cycles. Shorter releases may impact software development tasks such as refactoring in unintended ways. Not applying specific types of refactoring may also affect the software’s quality in the long term. Using this case study and past work on shorter releases, potential short release adopters can now better plan their transition to shorter releases knowing which areas of development may be affected.
Competition Neural Network Model Application in the Bilingual Teaching Quality Evaluation of Universities

Combined with the general principle of teaching evaluation and the characteristics of bilingual teaching, a more scientific and reasonable evaluation index system of bilingual teaching quality in colleges and universities is constructed. Based on the advantages of competitive neural network in adaptive learning of pattern classification, this paper presents an evaluation method of bilingual teaching quality in colleges and universities based on competitive network, and takes the bilingual course “Port Planning and Layout” as an example to analyze and calculate, which proves the feasibility and effectiveness of the application of the evaluation method.
A Novel Hybrid Tabu Search Algorithm With Binary Differential Operator for Knapsack Problems

The knapsack problem (KP) is a typical NP- complete problem. In this paper, a novel tabu search (TS) algorithm with the elite set is presented to solve the KPs. The algorithm is a hybrid TS with a binary differential operator (BDO), which is denoted by TSBDO. The binary differential operator is employed to mutate individuals which are generated randomly from the list EliteSol. To validate the proposed TSBDO algorithm, 15 high-dimensional KP examples are randomly generated. Simulated results indicate that the TSBDO algorithm can yield better solutions than some existing algorithms such as GMBO and BABC-DE.
Research on Foreground Object Recognition Tracking and Background Restoration in AIoT Era

With the rapid development of artificial intelligence technology and its combination with the Internet of things, recognition processing system has become a hot field of intelligent research. Meanwhile, with the popularity of SOC sensor, complex recognition algorithms are effectively implemented and utilized. In order to explore the characteristics and application scope of each foreground object tracking algorithm, this paper establishes foreground object tracking algorithms based on three kinds of Gaussian models and two kinds of Neural network models respectively, and solves the unique "delayed ghosting" phenomenon of Gaussian model with the total variational regularization (TV regularization). In the detection problem of camera jitter, a speed up robust feature (SURF) is added to optimize the model. On this basis, the kernel density estimation (KDE) is used for automatic threshold selection, and a secondary optimized foreground object recognition and tracking model is obtained. The results show that the recognition rate of the optimized mixture of Gaussian (MOG) model is more than 90% in static recognition, the recognition and tracking accuracy of the neural network model is higher than that of the Gaussian model in dynamic background, and the fuzzy self-organizing background subtraction (F-SOBS) can greatly overcome the camera jitter problem.
Incorporate Maximum Mean Discrepancy in Recurrent Latent Space for Sequential Generative Model

Stochastic recurrent neural networks have shown promising performance for modeling complex sequences. Nonetheless, existing methods adopt KL divergence as distribution regularizations in their latent spaces, which limits the choices of models for latent distribution construction. In this paper, we incorporate maximum mean discrepancy in the recurrent structure for distribution regularization. Maximum mean discrepancy is able to measure the difference between two distributions by just sampling from them, which enables us to construct more complicated latent distributions by neural networks. Therefore, our proposed algorithm is able to model more complex sequences. Experiments conducted on two different sequential modeling tasks show that our method outperforms the state-of-the-art sequential modeling algorithms.
Snipperclips: Cutting tools into desired polygons using themselves

We study Snipperclips, a computer puzzle game whose objective is to create a target shape with two tools. The tools start as constant-complexity shapes, and each tool can snip (i.e., subtract its current shape from) the other tool. We study the computational problem of, given a target shape represented by a polygonal domain of n vertices, is it possible to create it as one of the tools' shape via a sequence of snip operations? If so, how many snip operations are required? We consider several variants of the problem (such as allowing the tools to be disconnected and/or using an undo operation) and bound the number of operations needed for each of the variants.
“I Can’t Talk Now”: Speaking with Voice Output Communication Aid Using Text-to-Speech Synthesis During Multiparty Video Conference

COVID-19 has resulted in the rapid popularization of video conferencing. A growing number of users have become obligated to find suitable places for video conferencing, but sometimes they inevitably participate in unsuitable conditions such as noisy or too silent public spaces. However, the video conference experience according to the environment users are in has not been sufficiently discussed. In particular, there is no conducted research on the occasions where video conferencing participants feel unable to speak with their voice due to spatial factors and how to address these situations. In this study, we propose a voice output communication aid (VOCA) for video conferencing which allows users to chat without making a sound. We made a technology probe and conducted a user test. Users who feel unable to speak orally could participate more actively with VOCA. Based on the results, we described the effects and potential of VOCA for video conferencing.
Visualization Techniques For Prepositional-Case System In Teaching Russian Language For Foreign Students

The paper presents one of the possible solutions to the urgent problem teachers face when teaching Russian as a foreign language for engineering students at the faculty of pre-university training.The problem is associated with the need for improving the literacy of students and eliminating mistakes related to confusion or ignorance of the meanings of Russian cases.The authors used their teaching experience to develop a methodology that employs innovative fishbone and mind map techniques in order to visualize the meanings of the six Russian cases and the constructions with these case meanings.The methodology was developed with due regard to the peculiarities of thinking and perception of information by students of engineering specialties and the requirements of the modern educational paradigm that exhibits a gradual replacement of linear texts with polycode ones.The paper describes the two innovative techniques adapted by the authors for teaching RFL, substantiates the methodological expediency of using the fishbone diagram at A1 level and the mind map at A2 and B1 levels, and proposes an algorithm for working with these techniques at different levels of teaching Russian as a foreign languageelementary, basic and the first certification level.The experimental results showed the effectiveness of the described techniques, which provide the formation of systemic knowledge about the prepositional-case system of the Russian language and the communicative competencies of foreign students of engineering specialties.
A Secure Searchable Image Retrieval Scheme with Correct Retrieval Identity

As the image retrieval scheme continues to mature, many areas such as industrial and business have begun to apply more user-friendly techniques, namely image retrieval, instead of the text data retrieval technology. However, in some applications, the phenomenon of insufficient privacy protection for user images has been exposed. Aiming at the issue of user privacy, we proposed a Secure searchable image retrieval scheme with correct retrieval identity. We use the elliptic curve cryptography to realize the identification of entity identities in our scheme. Pre-filter tables can be constructed by local sensitive hash functions to build to optimize retrieval efficiency. The results of our experiments show that the statistical information of image data can be well protected and the entity identities in the system can be correctly identified. Due to the introduction of the pre-filtering table, the efficiency of image retrieval is higher than that of linear retrieval.
Internalization of the educational policy of the Russian Federation cooperation

The article is meant to analyze the process of internalization of the educational policy in the Russian Federation. Internationalization of the Russian education system is becoming one of the main trends in state policy of the Russian Federation, as evidenced by the recent launch of such programs as "5–100" and "Development of the Export Potential of the Russian System of Education". The authors believe that it is necessary to further align Russian and international educational standards (both Western and Eastern/Asian), create and develop applied bachelor’s degrees, as well as educational programs aimed at achieving the UN sustainable development goals (SDGs), which will be in demand in any country across the world. In this regard, the authors propose a number of measures that would contribute to achieving the strategic goals of the Russian Federation in the area of education services export, including joint programs of Russian, Chinese, Singaporean and Hong Kong universities in natural sciences and information and communication technologies. Taking into account Russia’s world leadership in the development of COVID-19 vaccine and studies related to the eff ective treatment of this disease, it would be advisable to adopt appropriate recommendations for the universities where foreign citizens are studying, explaining the risks of coronavirus infection spread during their studies, the procedure of medical insurance for students and guarantees of Russian universities in case of an appropriate insurance event.
Facilitating Connector Evolution With Architecture-Centric Development

Software architecture describes the structure of a system in terms of its components and connectors. In recent years, connector evolution has become a key concern in the architectural evolution of software systems. Evolving connectors is challenging because it involves modifying many lines of code spread across multiple files-a tedious and error-prone task. We propose an architecture-centric development approach that decouples connectors from functional code, making it easy to change or evolve connectors. Our approach involves integrating the architecture specification of a system with its implementation. We evaluate its effectiveness through a case study migrating connectors in a moderate-sized robotics software system.
PSDNet: A Balanced Architecture of Accuracy and Parameters for Semantic Segmentation

In this paper, we present our Pyramid Pooling Module (PPM) with SE1Cblock and D2SUpsample Network (PSDNet), a novel architecture for accurate semantic segmentation. Started from the known work called Pyramid Scene Parsing Network (PSPNet), PSDNet takes advantage of pyramid pooling structure with channel attention module and feature transformation module in Pyramid Pooling Module (PPM). The enhanced PPM with these two components can strengthen context information flowing in the network instead of damaging it. The channel attention module we mentioned is an improved “Squeeze and Excitation with 1D Convolution” (SE1C) block which can explicitly model interrelationship between channels with fewer number of parameters. We propose a feature transformation module named “Depth to Space Upsampling” (D2SUpsample) in the PPM which keeps integrity of features by transforming features instead of interpolating features, at the same time reducing parameters. In addition, we introduce a joint strategy in SE1Cblock which combines two variants of global pooling without increasing parameters. Compared with PSPNet, our work achieves higher accuracy on public datasets with 73.97% mIoU and 82.89% mAcc accuracy on Cityscapes Dataset based on ResNet50 backbone.
How Similar or Different is Rakugo Speech Synthesizer to Professional Performers?

We have been working on speech synthesis for rakugo (a traditional Japanese form of verbal entertainment similar to one-person stand-up comedy) toward speech synthesis that authentically entertains audiences. In this paper, we propose a novel evaluation methodology using synthesized rakugo speech and real rakugo speech uttered by professional performers of three different ranks. The naturalness of the synthesized speech was comparable to that of the human speech, but the synthesized speech entertained listeners less than the performers of any rank. However, we obtained some interesting insights into challenges to be solved in order to achieve a truly entertaining rakugo synthesizer. For example, naturalness was not the most important factor, even though it has generally been emphasized as the most important point to be evaluated in the conventional speech synthesis field. More important factors were the understandability of the content and distinguishability of the characters in the rakugo story, both of which the synthesized rakugo speech was relatively inferior at as compared with the professional performers. We also found that fundamental frequency (f <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</inf> ) modeling should at least be further improved to better entertain audiences. These results show important steps to reaching authentically entertaining speech synthesis.
A Seamless Natural Locomotion Concept for VR Adventure Game "The Amusement"

The Amusement is a story-driven VR adventure about an old abandoned amusement park in the 1920s. The main game mechanics are exploration and discovery. Therefore, one of the most unique points of this game is the locomotion concept. It was designed with the following goal: The whole park can be explored entirely with natural locomotion techniques to create a seamless fusion of exploration and discovery. To achieve this, The Amusement leverages a mix of different techniques: 1) Redirected Walking techniques [1] such as rotation gains [2] and impossible spaces [3], 2) Passive movement: lifts, platforms, vehicles, etc., 3) Indirect movement: for example climbing, pulling a rope to move a raft, moving a handle of a railroad trolley, etc. Furthermore, techniques are extended by or coupled with physical interactions or movements like crawling, jumping or sliding around corners. A seamless integration of all these techniques enables the player to walk around freely in the recommended room-scale play space of 2m X 2m, while exploring a potentially infinite virtual space.
Foreground-focused domain adaption for object detection

Object detectors suffer from accuracy loss caused by domain shift from a source to a target domain. Unsupervised domain adaptation (UDA) approaches mitigate this loss by training with unlabeled target domain images. A popular processing pipeline applies adversarial training that aligns the distributions of the features from the two domains. We advocate that aligning the full image level features is not ideal for UDA object detection due to the presence of varied background areas during inference. Thus, we propose a novel foreground-focused domain adaptation (FFDA) framework which mines the loss of the domain discriminators to concentrate on the backpropagation of foreground loss. We obtain mining masks by collecting target predictions and source labels to outline foreground regions, and apply the masks to image and instance level domain discriminators to allow backpropagation only on the mined regions. By reinforcing this foreground-focused adaptation throughout multiple layers in the detector model, we gain a significant accuracy boost on the target domain prediction. Compared to previous methods, our method reaches the new state-of-the-art accuracy on adapting Cityscape to Foggy Cityscape dataset and demonstrates competitive accuracy on other datasets that include various scenarios for autonomous driving applications.
Feature-Supervised Action Modality Transfer

This paper strives for action recognition and detection in video modalities like RGB, depth maps or 3D-skeleton sequences when only limited modality-specific labeled examples are available. For the RGB, and derived optical-flow, modality many large-scale labeled datasets have been made available. They have become the de facto pre-training choice when recognizing or detecting new actions from RGB datasets that have limited amounts of labeled examples available. Unfortunately, large-scale labeled action datasets for other modalities are unavailable for pre-training. In this paper, our goal is to recognize actions from limited examples in non-RGB video modalities, by learning from large-scale labeled RGB data. To this end, we propose a two-step training process: (i) we extract action representation knowledge from an RGB-trained teacher network and adapt it to a non-RGB student network. (ii) we then fine-tune the transfer model with available labeled examples of the target modality. For the knowledge transfer we introduce feature-supervision strategies, which rely on unlabeled pairs of two modalities (the RGB and the target modality) to transfer feature level representations from the teacher to the student network. Ablations and generalizations with two RGB source datasets and two non-RGB target datasets demonstrate that an optical-flow teacher provides better action transfer features than RGB for both depth maps and 3D-skeletons, even when evaluated on a different target domain, or for a different task. Compared to alternative cross-modal action transfer methods we show a good improvement in performance especially when labeled non-RGB examples to learn from are scarce.
Motion Segmentation with Pairwise Matches and Unknown Number of Motions

In this paper we address motion segmentation, that is the problem of clustering points in multiple images according to a number of moving objects. Two-frame correspondences are assumed as input without prior knowledge about trajectories. Our method is based on principles from “multi-model fitting” and “permutation synchronization”, and - differently from previous techniques working under the same assumptions - it can handle an unknown number of motions. The proposed approach is validated on standard datasets, showing that it can correctly estimate the number of motions while maintaining comparable or better accuracy than the state of the art.