Reviewerly/exHarmony huggingface dataset:
LASSO Regularization within the LocalGLMnet Architecture

Deep learning models have been very successful in the application of machine learning methods, often out-performing classical statistical models such as linear regression models or generalized linear models. On the other hand, deep learning models are often criticized for not being explainable nor allowing for variable selection. There are two different ways of dealing with this problem, either we use post-hoc model interpretability methods or we design specific deep learning architectures that allow for (more) easy interpretation and explanation. This paper builds on our previous work on the LocalGLMnet architecture that gives an interpretable deep learning architecture. In the present paper, we show how group LASSO regularization (and other regularization schemes) can be implemented within the LocalGLMnet architecture so that we receive feature sparsity for variable selection. We benchmark our approach with the recently developed LassoNet of Lemhadri et al.
Critic Boosting Attention Network on Local Descriptor for Few-shot Learning

Few-shot learning is a new upsurge of research in the computer vision field to adapt to new tasks with only a few examples. Recent works commonly use meta-learning or metric learning methods to solve the problem. In contrast, many of them usually use image-level feature representations and treat all parts of the image equally. This manipulation cannot cast off the cluttered background's interference or grasp the key-points of the idea rapidly. In our study, the authors use local descriptors assisted with image-to-class metric learning measures and utilize a critic following the human visual system mechanism to force the model to concentrate on the image's essential semantic parts. The unsupervised critic gives each of the local descriptors of an input image a score evaluating its significance and contribution to the final classification. Thus the model can relieve from the image background cluttering and fast focus on the essential parts. Generally, our proposed critic boosting attention network (short as CBAnet) can be classified as a kind of attention mechanism. Still, we do not need to use the background impact in an unsupervised way. The extensive experiments that we conduct on several benchmarks testify to our proposed method's superiority for few-shot learning.
ICDAR 2021 Competition on Components Segmentation Task of Document Photos

This paper describes the short-term competition on "Components Segmentation Task of Document Photos" that was prepared in the context of the "16th International Conference on Document Analysis and Recognition" (ICDAR 2021). This competition aims to bring together researchers working on the filed of identification document image processing and provides them a suitable benchmark to compare their techniques on the component segmentation task of document images. Three challenge tasks were proposed entailing different segmentation assignments to be performed on a provided dataset. The collected data are from several types of Brazilian ID documents, whose personal information was conveniently replaced. There were 16 participants whose results obtained for some or all the three tasks show different rates for the adopted metrics, like "Dice Similarity Coefficient" ranging from 0.06 to 0.99. Different Deep Learning models were applied by the entrants with diverse strategies to achieve the best results in each of the tasks. Obtained results show that the current applied methods for solving one of the proposed tasks (document boundary detection) are already well stablished. However, for the other two challenge tasks (text zone and handwritten sign detection) research and development of more robust approaches are still required to achieve acceptable results.
Modeling Global Semantics for Question Answering over Knowledge Bases

Query graph as a junction of semantic parsing in question answering over knowledge bases (KBQA) connects questions and logical queries. Though query graph consists of rich information such as structure, relation, etc., the current KBQA's models mainly utilize limited relation information in a naive way. It is not easy to learn the representation of a query graph with that information due to the heterogeneity of the query graph and intricate correlation of relations. In this paper, we propose a Global Semantic-based Message Passing (GSMP) model to model the global semantics of a query graph from its structure and relation information. In GSMP, we present a recurrent-based relational graph convolutional network (RGCN) to capture heterogeneous query graphs where the recurrent unit improves the capability of RGCN in processing small-scale query graphs. Moreover, we present a contextual-based method to remove ambiguity caused by intricate correlations where the contextual adjacency of relations optimizes relation representation. Finally, we present a nonlinear gate-based encoder to learning the representation of questions' syntactic tree, as the structure information of questions, for better matching the global semantics of query graphs. Experiments evaluated on benchmarks show that our model outperforms off-the-shelf models.
DoubleHigherNet: Coarse-to-Fine Precise Heatmap Bottom- Up Dynamic Pose Computer Intelligent Estimation

Accurate keypoint positioning is necessary for bottom-up multi-person pose estimation methods to handle scale variation and crowdedness. In this paper, we present DoubleHigherNet: a novel network learning scale-aware and precise heatmap representation for bottom-up process using double high-resolution feature pyramids and coarse-to-fine training. The two feature pyramids in DoubleHigherNet consists of 1/4 resolution feature and higher-resolution (1/2) maps generated by attention fusion blocks and transposed convolutions. Benefited by the training strategy, muti-resoltion and coarse-fine heatmap aggregation, the proposed approach is able to predict keypoints more accurately so as to perform better on difficult crowded scenes. DoubleHigherNet-w32 achieves competitive result on CrowdPose-test, surpassing all the top-down methods and bottom-up SOTA HigherHRNet-w32 (which possesses similar number of params with DoubleHigherNet-w32).
Buffer Displacement Based Online Learning Algorithm For Low Latency HTTP Adaptive Streaming

Live streaming has gained more and more interest in recent years, where low latency adaptive bitrate(ABR) algorithms play a vital role in providing high quality of experience(QoE). However, low latency requires a small buffer of the player, which makes it harder to achieve high bitrate while keeping a low rebuffer rate. Most proposed solutions that depend on reliable bandwidth predictions usually result in high rebuffer rates and frequent bitrate switches. In this paper, we propose a buffer displacement based online learning algorithm called BDA for low latency adaptive streaming utilizing convex optimization. Unlike traditional buffer-based ABRs that take absolute buffer lengths as input, BDA makes bitrate decisions based on the real-time buffer displacement. We evaluate BDA with four other state-of-the-art ABRs under various throughput traces and target latency settings. Compared to four other ABRs, BDA reduces bitrate switch frequency by 52.9%, increases average bitrate by 18.3% and achieves 23.1% improvement in total QoE.
SmartCamera: Realtime Video Stream-Oriented Action Recognition Platform in Edge Environment

This paper designs a real-time video stream-oriented action recognition platform, namely SmartCamera. It aims to detect target actions in real-time in a resource-constrained edge environment. The SmartCamera adopts 3D convolutional network-based sliding window as the classification model of actions, and introduces a flexible sliding window localization method to reduce the computational complexity of action recognition without harming behavioral consistency. The prototype of SmartCamera mainly includes five modules: Real-time Video Stream Collection, Video Stream Preprocessing, Action Recognition, Sliding Window Localization, User Interface. The preliminary evaluation shows that the system can detect target actions with an acceptable accuracy and latency.
Comprehensive Comparison of LSTM Variations for the Prediction of Chaotic Time Series

Chaos theory is a field of study used to model the irregular fluctuations encountered in all areas of our daily life. Time series obtained from chaotic systems such as currencies, stock markets, and weather conditions are called chaotic time series. Making predictions from these time series has recently attracted the attention of researchers as it will eliminate the need for complex mathematical models. Recently, deep neural networks are widely used in the prediction of chaotic time series as well as in solving many problems in the literature. In this study, time series obtained from three different chaotic systems were collected in order to predict chaotic time series, and four different LSTM variations were used to predict these data. As a result of the experimental studies, it has been observed that Stacked LSTM predicts more successfully than other LSTM variations. The obtained 3.7397x10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> validation RMSE value and 0.1558 test RMSE value show that Stacked LSTM gives more accurate results than other methods used in the prediction of chaotic time series in the literature.
Low Light GAN-Based Photo Enhancement

This paper is based on a novel learning-based pipeline that replaces the conventional image processing pipeline in order to enhance performance of current multi camera devices for low light photography. This paper proposes a new suggestive algorithm to suggest a better position to capture image based on region segmentation (object-based segmentation), camera calibration, parameter selection, Frame Regeneration (Merging and alignment of frames) and then using Generative Adversarial Network (GAN) for image enhancement. As Low Light Image enhancement is one of the biggest digital image processing problem due to factors like noise, low exposure and incorrect edge detections of the object in low light so image enhancement is difficult these images, this research work proposes a low light image enhancement model by comparing it with current models. Also, the proposed research work is mainly focused on mobile devices as they are currently used by over 90% users to capture the images. The proposed method assists in increasing the SNR by 7.63 % over the other existing approaches.
The Effects of the Covid-19 Pandemic on Organized Crime Activities in the Society 5.0

The paper aims to analyze the effects that the Covid-19 pandemic has had on the illegal activities carried out by organized crime. Organized criminal groups have shown that they are able to quickly identify the sectors that can ensure greater earnings by exploiting the economic and health crisis due to the spread of Covid-19. The paper highlights how Society 5.0, through its inspiring principles, can offer valid tools for combating illegal activities carried out by organized crime.
Feasibility of Multi-Configuration Unmanned Aerial Vehicle for Last Mile Delivery of Medical Supplies

Access to critical medicines is hindered throughout the world due to inadequate transportation, communication, or supply chain infrastructure. Numerous immunization programs in underdeveloped and developing countries face countless challenges in getting life-saving vaccines to those who need them the most. During the Covid-19 period, the shortcomings of the medical supply chain and delivery system were evident. Unmanned aerial vehicles play a vital role in delivering medical supplies; however, conventional multi-rotors have a limited flight time, which defines the range of the missions, whereas a plane has the capability of conducting long-range missions. Shortcomings of using planes for the operation is the need for a runway, limiting its reach in an urban environment due to the unavailability of space. We are proposing a multi-configuration unmanned aerial vehicle for medical supplies delivery. It is also referred to as Vertical takeoff and landing (VTOL), which can take off, and land without the need for the runway. Furthermore has extended operational range compared to the multi-rotors given its functional behavior as a plane. We have emulated aerial vehicles' configurations with real-life mission parameters and compared them with our proposed Unmanned aerial vehicles model. The design, fabrication process of the multi-configuration unmanned aerial vehicle for the medical supplies delivery is explained in detail in this paper.
Embedding QR Code onto Triangulated Meshes using Horizon Based Ambient Occlusion

Abstract QR code is a widely used format to encode information through images that can be easily decoded using a smartphone. These devices play a significant role in most people's everyday lives, making the encoded information widely accessible. However, decoding the QR code becomes challenging when significant deformations occur in the label. An easy and quick solution to keep the deformation on a minimum level is to affix the label that contains the QR code onto a developable surface patch of a 3D model. The perspective distortion that can appear is efficiently dealt with during the decoding process. In recent years an alternative method has emerged. In the work of Kikuchi et al., the QR code is embedded onto B‐spline surfaces of CAD models to give more freedom in providing additional information. This method was further improved and extended by Peng et al. embed QR codes onto the surface of general meshes. This paper introduces a solution to embed QR codes onto the surface of general meshes without densely triangulating the selected area of the QR code. It uses the deferred shading technique to extract the surface normals and the depth values around the QR code's user‐given centre. We propose two methods for automatically finding the projection direction even when highly curved areas are selected based on the retrieved information while rendering the model. Besides, we introduce two methods needing a projection direction and a QR code centre to determine a size for automatically embedding the QR code. We propose patterns for decreasing the carving depth of the embedded QR codes, and we use the Horizon‐Based Ambient Occlusion to speed up the engraving process. We validate our method by comparing our results to the outcomes of Peng et al.
A Joint Optimization Framework of the Embedding Model and Classifier for Meta-Learning

The aim of meta-learning is to train the machine to learn quickly and accurately. Improving the performance of the meta-learning model is important in solving the problem of small samples and in achieving general artificial intelligence. A meta-learning method based on feature embedding that exhibits good performance on the few-shot problem was previously proposed. In this method, the pretrained deep convolution neural network was used as the embedding model of sample features, and the output of one layer was used as the feature representation of samples. The main limitation of the method is the inability to fuse low-level texture features and high-level semantic features of the embedding model and joint optimization of the embedding model and classifier. Therefore, a multilayer adaptive joint training and optimization method of the embedding model was proposed in the current study. The main characteristics of the current method include using multilayer adaptive hierarchical loss to train the embedding model and using the quantum genetic algorithm to jointly optimize the embedding model and classifier. Validation was performed based on multiple public datasets for meta-learning model testing. The proposed method shows higher accuracy compared with multiple baseline methods.
Accelerating Neural Network Training using Arbitrary Precision Approximating Matrix Multiplication Algorithms

Matrix multiplication is one of the bottleneck computations for training the weights within deep neural networks. To speed up the training phase, we propose to use faster algorithms for matrix multiplication known as Arbitrary Precision Approximating (APA) algorithms. APA algorithms perform asymptotically fewer arithmetic operations than the classical algorithm, but they compute an approximate result with an error that can be made arbitrarily small in exact arithmetic. Practical APA algorithms provide significant reduction in computation time and still provide enough accuracy for many applications like neural network training. We demonstrate that APA algorithms can be efficiently implemented and parallelized for multicore CPUs to obtain up to 28% and 21% speedups over the fastest implementation of the classical algorithm using one core and 12 cores, respectively. Furthermore, using these algorithms to train a Multi-Layer Perceptron (MLP) network yields no significant reduction in the training or testing error. Our performance results on a large MLP network show overall sequential and multithreaded performance improvements of up to 25% and 13%, respectively. We also demonstrate up to 15% improvement when training the fully connected layers of the VGG-19 image classification network.
An efficient replication management system for HDFS management

The term “replication” refers to the practice of keeping identical copies of data on multiple systems. Any distributed file system must have replication as one of its design requirements (DRS). For storing and processing data, Hadoop DFS is now used by the majority of academia and industry. The Hadoop distributed file system (HDFS) is a system for storage and processing huge amounts of information. In HDFS, inefficient replication is the main issue that contributes to a file system's performance degradation. Because of the system's robustness and dynamic features, the quantity of applications depending on Hadoop is rapidly growing. The HDFS, which is at the heart of Apache Hadoop, uses a static replication strategy to provide computation with reliability, scalability, and high availability. The admittance rate for each file in HDFS, however, is completely special due to the uniqueness of similar functions on various layers. As a result, using the similar replicating method for each file may have negative performance consequences. This paper proposes a method for dynamically replicating information files depending on predictive analysis after carefully considering the drawbacks of HDFS architecture. To address this issue, we propose an proficient data replication method in the Hadoop framework that increases the availability.
Readiness of higher school teachers to use digital technologies in the educational process

The digitalization of the education system imposes certain circumstances not only on the organization of the educational process and the training of specialists in demand in the modern world, but also on the skills of a teacher in the field of information and communication technologies. Within the framework of this article, an analysis of Russian and foreign approaches to defining a set of digital competencies necessary for a modern teacher of higher education is carried out. The purpose of this study is to determine an objective diagnostic toolkit for identifying the level of digital competencies among teachers of higher institutions. Based on the analysis of Russian and foreign experience in the development of digital competence among teachers, including a higher education teacher, an internal questionnaire was developed to assess the level of formation of the necessary digital skills. Data collection was carried out through an online survey of teachers using a structured questionnaire. A survey of university teachers was conducted in order to identify the readiness to use digital technologies in the educational process. The data obtained during the survey made it possible to obtain objective information about the level of formation of digital competencies among teachers and their readiness to use digital tools and technologies in the educational process. University teachers have demonstrated high results in such competencies as the use of digital solutions for effective communication of students on academic assignments; creating your own digital resources; creation and modification of digital educational materials, inclusion of students in group work using the Internet, including online services for joint project activities; taking into account the individual characteristics and needs of students in the educational process; exchange of ideas and materials with colleagues from other educational organizations, including through cloud systems, advanced training through online training.
A Pure View of Ecumenical Modalities

Recent works about ecumenical systems, where connectives from classical and intuitionistic logics can co-exist in peace, warmed the discussion on proof systems for combining logics. This discussion has been extended to alethic modalities using Simpson’s meta-logical characterization: necessity is independent of the viewer, while possibility can be either intuitionistic or classical. In this work, we propose a pure, label free calculus for ecumenical modalities, \(\mathsf {nEK}\), where exactly one logical operator figures in introduction rules and every basic object of the calculus can be read as a formula in the language of the ecumenical modal logic \(\mathsf {EK}\). We prove that \(\mathsf {nEK}\) is sound and complete w.r.t. the ecumenical birelational semantics and discuss fragments and extensions.
Privacy-preserving policy evaluation in multi-party access control

Recent years have seen an increasing popularity of online collaborative systems like social networks and web-based collaboration platforms. Collaborative systems typically offer their users a digital environment in which they can work together and share resources and information. These resources and information might be sensitive and, thus, they should be protected from unauthorized accesses. Multi-party access control is emerging as a new paradigm for the protection of co-owned and co-managed resources, where the policies of all users involved in the management of a resource should be accounted for collaborative decision making. Existing approaches, however, only focus on the jointly protection of resources and do not address the protection of the individual user policies themselves, whose disclosure might leak sensitive information. In this work, we propose a privacy-preserving mechanism for the evaluation of multi-party access control policies, which preserves the confidentiality of user policies while remaining capable of making collaborative decisions. To this end, we design secure computation protocols for the evaluation of policies in protected form against an access query and realize such protocols using two privacy-preserving techniques, namely Homomorphic Encryption and Secure Functional Evaluation. We show the practical feasibility of our mechanism in terms of computation and communication costs through an experimental evaluation.