Reviewerly/exHarmony huggingface dataset:
Human Pose Estimation Using Artificial Intelligence

Artificial intelligence is currently grabbing the attention of the developers, reason being that its vast applications. One of such applications is Human Pose Estimation. Estimating postures of human is amongst the trending topics in the research field nowadays. In this, the system is provided with the trained model and by the help of which the system can look for joints in body of the person standing in front of it. It has vast applications like it can be utilized for checking fitness of an athlete, to check if a person is doing exercise properly or not. In this paper we discuss modelling a gym tracker using artificial intelligence. Here we are counting repetitions of 4 exercises: squats, pushups, curls, pullups.
Joining Aspect Detection and Opinion Target Expression Based on Multi-Deep Learning Models

Aspect-based Sentiment Analysis (ABSA) is an advanced task as well as technique which is developed based on sentiment analysis. Aspect-based sentiment analysis is a text analysis technique that categorizes data by aspect and identifies the sentiment attributed to each one. Aspect-based sentiment analysis can be used to analyze customer feedback data by associating specific sentiments with different aspects (e.g. the attributes or components) of a product or service. This has attracted increasing attention in the recent few years in Natural Language Processing and has broad applications in both research and business. In this research, we apply joining aspect detection and opinion target expression using multi-deep learning methods: RNN, LSTM and CNN and we do experiments on Vietnamese VLSP2018 dataset. Both of the results for the aspect detection as well as opinion target expression achieve the best results on the CNN model.
Recommender System Based on Network Structure Link Analysis Technique Through Community Detection in Social Network to Handle the Cold-Start Problem

The recommendation system plays a pivotal role in electronic commerce sites to trade their products and services to consumers. The system further helps in identifying the items for the user and might be worthful for creating buying options that are purely rely on recommender. Especially, in the case of a book recommender system, it is necessary to recommend the books to learn in conformity with the learning environment of the learner. The prime objective is to propose an efficient book recommender system for social networking communities. Moreover, the paper addresses all the important parameters required to compute the efficiency of the system. The authors also outlined existing approaches utilize in book recommendation system along with major findings. The author took one of the fundamental challenges of cold-start problem and proposed a new book recommender system’s framework using the “link analysis technique” of network structure. The proposed structure utilizes a hybrid technique for a book recommender system. Besides, the paper has tremendous potential to appraise the reader to explore a new book recommender system which further suggests the books to a novice on the social network as well as in-network communities.
Multivariate Partially Blind Signature Scheme

In e-cash, blind signature provides perfect anonymity, which causes the threat of managing an infinite database to prevent double-spending of e-coins. Partial blind signature is used to tackle this problem using a piece of common information shared between the signer and the user. In the family of multivariate public key cryptography, there is no partially blind signature till now. We here design the first multivariate partially blind signature deploying the Rainbow signature scheme and MQDSS signature scheme. Moreover, we discuss partial blindness and one more unforgeability of the proposed scheme, along with its efficiency.
Retrospective Study of Convolutional Neural Network for Medical Image Analysis and a Deep Insight Through Histopathological Dataset

Convolutional neural network (CNN) has become a prominent technology of choice in medical image analysis as CNN is easy to train and requires less pre-processing of data. The paper elaborates the concept of CNN with architectural details and their application in medical imaging to resolve the problems of detection, segmentation and classification. Limited dataset, scarcity of annotated data, non-standardized evaluation metric and model biasing are being observed as major challenges associated with the implementation of CNNs in medical image analysis. The emerging and advanced techniques to address the confronted challenges are also explored to improve the CNN performance. A brief study on BreakHis dataset classification using the fine-tuning approach of transfer learning with AlexNet is also demonstrated to select an optimal layer for fine-tuning for both binary and multi-classification task. The experimental results validate the superiority of network fine-tuning at a moderate level for the task of classifying the magnification independent histology images.
Self-education of pedagogues working with disabled children under conditions of a preschool educational organisation

The article deals with the issues of increasing the professional competence of a pedagogue of a preschool educational institution in the framework of self-education. The purpose of the study is to identify the level of formation of self-education activities of pedagogues working in a preschool educational organisation where disabled children are brought up, depending on their skill level. The study used a set of pedagogic methods – questioning and analysis of pedagogues' documentation (plans and reports on the self-education topic). The typical difficulties of pedagogues associated with the activities of self-education at the information-preparatory, practical and final stages of self-education are analysed – most pedagogues of a preschool educational organisation of a compensatory or combined type experience difficulties in choosing and formulating a relevant topic in self-education, in setting goals and objectives; in determining the stages of self-education, in drawing up a personal plan for self-education; in determining the forms of self-education; in the selection of scientific and methodological literature, including the Internet resources, the regulatory framework in accordance with the topic of self-education; difficulties associated with information processing of the material read (highlighting the main thing, systematisation, analytical description using scientific terminology); difficulties in applying innovative methods and techniques in working with children, taking into account the level of their speech development, open classes and events, master classes; difficulties associated with the publication of their own practical experience in scientific, methodological and electronic publications; difficulties in preparing a report on the topic of self-education and in its presentation, including the use of information and communication technologies. Conclusions are drawn about the development of ways to effectively organise the work of self-education of pedagogues within the framework of a preschool educational institution, compensatory and combined types
Secondary vocational education: necessity of structural changes

The domestic system of secondary vocational education (SPE), despite the declared growth in its popularity in recent years, is systematically criticized for inertia and lagging behind technological changes in the economy and, as a result, not meeting its need for qualified workers. The quantitative accumulation of negative factors dictates the need for qualitative structural changes in the STR system. The state program measures planned earlier and not completed to date have not yielded positive results in improving the efficiency of its functioning. The proposed new project – "Professionalism", positioned as a new level of education, can be considered as an opportunity for a comprehensive restructuring of the system of vocational education in Russia. Based on the methods of comparative and retrospective analysis of statistical data from Russian and international studies, content analysis of the websites of SPO institutions, a conclusion is made about the need for structural changes in the SPO system. One of the main results of the study was the analysis of the reasons for the asynchronous functioning of the SPO system and the personnel needs of enterprises, as well as possible options for organizing the educational process in vocational education institutions within the framework of the "Professionalism" project. The practical significance of the research is to demonstrate the possibilities of retrospective, prognostic and comparative analyses in assessing the activities of SPE institutions and their transformation into other educational systems.
Style Adaptive Semantic Image Editing with Transformers

The goal of semantic image editing is to modify an image based on an input semantic label map, to carry out the necessary image manipulation. Existing approaches typically lack control over the style of the editing, resulting in insufficient flexibility to support the desired level of customization, e.g., to turn an object into a particular style or to pick a specific instance. In this work, we propose Style Adaptive Semantic Image Editing (SASIE), where a reference image is used as an additional input about style, to guide the image manipulation process in a more adaptive manner. Moreover, we propose a new transformer-based architecture for SASIE, in which intra-/inter-image multi-head self-attention blocks transfer intra-/inter-knowledge. The content of the edited areas is synthesized according to the given semantic label, while the style of the edited areas is inherited from the reference image. Extensive experiments on multiple datasets suggest that our method is highly effective and enables customizable image manipulation.
Semi-supervised Domain Adaptation by Similarity Based Pseudo-Label Injection

One of the primary challenges in Semi-supervised Domain Adaptation (SSDA) is the skewed ratio between the number of labeled source and target samples, causing the model to be biased towards the source domain. Recent works in SSDA show that aligning only the labeled target samples with the source samples potentially leads to incomplete domain alignment of the target domain to the source domain. In our approach, to align the two domains, we leverage contrastive losses to learn a semantically meaningful and a domain agnostic feature space using the supervised samples from both domains. To mitigate challenges caused by the skewed label ratio, we pseudo-label the unlabeled target samples by comparing their feature representation to those of the labeled samples from both the source and target domains. Furthermore, to increase the support of the target domain, these potentially noisy pseudo-labels are gradually injected into the labeled target dataset over the course of training. Specifically, we use a temperature scaled cosine similarity measure to assign a soft pseudo-label to the unlabeled target samples. Additionally, we compute an exponential moving average of the soft pseudo-labels for each unlabeled sample. These pseudo-labels are progressively injected (or removed) into the (from) the labeled target dataset based on a confidence threshold to supplement the alignment of the source and target distributions. Finally, we use a supervised contrastive loss on the labeled and pseudo-labeled datasets to align the source and target distributions. Using our proposed approach, we showcase state-of-the-art performance on SSDA benchmarks - Office-Home, DomainNet and Office-31. The inference code is available at https://github.com/abhayraw1/SPI
Open-Vocabulary Semantic Segmentation Using Test-Time Distillation

Semantic segmentation is a key computer vision task that has been actively researched for decades. In recent years, supervised methods have reached unprecedented accuracy; however, obtaining pixel-level annotation is very time-consuming and expensive. In this paper, we propose a novel open-vocabulary approach to creating semantic segmentation masks, without the need for training segmentation networks or seeing any segmentation masks. At test time, our method takes as input the image-level labels of the categories present in the image. We utilize a vision-language embedding model to create a rough segmentation map for each class via model interpretability methods and refine the maps using a test-time augmentation technique. The output of this stage provides pixel-level pseudo-labels, which are utilized by single-image segmentation techniques to obtain high-quality output segmentations. Our method is shown quantitatively and qualitatively to outperform methods that use a similar amount of supervision, and to be competitive with weakly-supervised semantic-segmentation techniques.
Cancer detection for small-size and ambiguous tumors based on semantic FPN and transformer

Early detection of tumors has great significance for formative detection and determination of treatment plans. However, cancer detection remains a challenging task due to the interference of diseased tissue, the diversity of mass scales, and the ambiguity of tumor boundaries. It is difficult to extract the features of small-sized tumors and tumor boundaries, so semantic information of high-level feature maps is needed to enrich the regional features and local attention features of tumors. To solve the problems of small tumor objects and lack of contextual features, this paper proposes a novel Semantic Pyramid Network with a Transformer Self-attention, named SPN-TS, for tumor detection. Specifically, the paper first designs a new Feature Pyramid Network in the feature extraction stage. It changes the traditional cross-layer connection scheme and focuses on enriching the features of small-sized tumor regions. Then, we introduce the transformer attention mechanism into the framework to learn the local feature of tumor boundaries. Extensive experimental evaluations were performed on the publicly available CBIS-DDSM dataset, which is a Curated Breast Imaging Subset of the Digital Database for Screening Mammography. The proposed method achieved better performance in these models, achieving 93.26% sensitivity, 95.26% specificity, 96.78% accuracy, and 87.27% Matthews Correlation Coefficient (MCC) value, respectively. The method can achieve the best detection performance by effectively solving the difficulties of small objects and boundaries ambiguity. The algorithm can further promote the detection of other diseases in the future, and also provide algorithmic references for the general object detection field.
A novel secure data outsourcing scheme based on data hiding and secret sharing for relational databases

Abstract Data encryption‐based and secret sharing‐based data outsourcing schemes protect the confidentiality of sensitive attributes but not their secrecy. Ciphertexts/shares generated by a data encryption/secret sharing scheme can attract the attention of interceptors. Thus, it is desired to hide the existence of highly‐sensitive attributes (as secret attributes) in the outsourced relations in addition to protecting their contents. This paper proposes a novel scheme that integrates data hiding with secret sharing for relational databases to protect both the secrecy and confidentiality of secret attributes. It embeds one or multiple secret attributes in a relation into one or multiple cover attributes in the same relation. A set of share (and possibly index) columns are constructed such that they are pretended to be associated with only the cover attributes, while those share columns and some virtual share columns can be used to recover both the secret and cover attributes. What interceptors observe in each relation include the attributes stored in plaintext and the share (and possibly index) columns associated with the cover attributes but not any extra column. Thus, they find nothing suspicious. This is the first effective data hiding scheme for relational databases that protects the secrecy of secret attributes.
A portable English translation system through deep learning and internet of things

Portable English translation systems are increasingly important in daily life. Existing online English translation based on Transformer has become mainstream on the service side. But Transformer loses the ability to capture local dependencies, leading to local syntax errors. The graph convolution operation can fully model the local relationship. Therefore, this paper proposes a new graph embedded Transformer network (GETN) for Portable English translation based on Internet of Things technology. Specifically, the portable device converts the voice signal into text, and then transmits it to the server through Internet technology. By introducing the graph convolution module into the existing Transformer, local relations can be effectively modeled. The results of the existing English grammatical error correction dataset show that the graph‐embedded mechanism can achieve a higher translation effect with fewer parameters and effectively alleviate local syntax errors.
Timber–Prairie Wolf Optimization-Dependent Deep Learning Classifier for Anomaly Detection in Surveillance Videos

Anomaly detection in public places using the video surveillance gains significance due to the real-time monitoring and security that ensures the personal assets and public security. Accordingly, in this research, a deep CNN model with Timber–Prairie wolf optimization algorithm (TPWO) optimization is proposed for surveillance-based anomaly detection. To support the TPWO-based deep CNN anomaly detection model, tracking model named OptSpatio tracking model tracks the location and movement of the anomalous objects in any locality. The OptSpatio tracking model uses both visual and spatial tracking models to monitor any anomalous activity. On the other hand, TPWO is designed to tune the deep classifier for acquiring better detection performance. The TPWO-based model surpasses the competent methods in terms of accuracy by 97.214%, sensitivity by 97.831% and specificity by 96.668% with minimal EER of 2.786%. The MOTP values are also obtained at a rate of 0.7325; moreover, the effectiveness of the TPWO method is justified at the object-, frame-, and pixel-level analysis.
Preparing maximally entangled states by monitoring the environment-system interaction

A common assumption in an open quantum system is that the noise induced by the environment, due to the interaction between a quantum system and its environment, is responsible for the disappearance of quantum properties. Interestingly, Barreiro et al. [Nature 470 (2011) 486] show, experimentally, using an open-system quantum ion traps simulator, an environment state can be engineered and controlled to pump an arbitrary quantum system toward a maximally entangled state and thus can be considered a resource for quantum information processing. Here, we demonstrate this idea to pump an arbitrary maximally mixed state into Greenberger–Horne–Zeilinger (GHZ) state by simulating our quantum circuit using one of the IBM Q processors. Barreiro et al. [Nature 470 (2011) 486] offer the circuits for the execution of the GHZ pumping. Nevertheless, those are collected gates that are suitable to the trapped-ions platform, so their circuits implementation on the IBM Q devices would result in large depth circuits and thus requires many gates. Consequently, we suggest a different circuit structure that follows a similar goal, but has been considered the features of the IBM Q platform. Moreover, we run the simulation of our circuit using the QASM simulator and free-web-based interface, IBM Quantum Experience, with and without error mitigation, to investigate the effect of the noise on the preparation of the initial mixed states of the qubits in addition to the population of the target state of the system.
On a variant of Pillai’s problem with binary recurrences and S-units

Let [Formula: see text] be a fixed binary recurrence with real characteristic roots [Formula: see text] satisfying [Formula: see text] and let [Formula: see text] be fixed distinct prime numbers. In this paper, we show that there exist effectively computable, positive constants [Formula: see text] and [Formula: see text] such that the Diophantine equation [Formula: see text] has at most [Formula: see text] solutions [Formula: see text] if [Formula: see text] and at most [Formula: see text] solutions [Formula: see text] if [Formula: see text]. In order to demonstrate the strength of our method we show that for the binary recurrence sequence [Formula: see text], with [Formula: see text] for [Formula: see text], [Formula: see text] and [Formula: see text], the Diophantine equation [Formula: see text] has at most six solutions [Formula: see text] unless [Formula: see text] in which case it has exactly seven solutions.
Knowledge Graph-Based Integration of Autonomous Driving Datasets

Autonomous Driving (AD) datasets, when used in combination with deep learning techniques, have enabled significant progress on difficult AD tasks such as perception, trajectory prediction, and motion planning. These datasets represent the content of driving scenes as captured by various sensors, including cameras, RADAR, and LiDAR, along with 2D/3D annotations of traffic participants. Such datasets, however, often fail to capture and to represent the spatial, temporal, and semantic relations between entities in a scene. This lack of knowledge leads to a shallow understanding of the true complexity and dynamics inherent in a driving scene. In this paper, we argue that a Knowledge Graph (KG)-based representation of driving scenes, that provides a richer structure and semantics, will lead to further improvements in AD. Towards this goal, we developed a layered architecture and ontologies for specific AD datasets and a fundamental ontology of shared concepts. We also built KGs for three different AD datasets. We perform an analysis with respect to information contained in the AD KGs and outline how the additional semantic information contained in the KGs could improve the performance of different AD tasks. Moreover, example queries are provided to retrieve relevant information that can be exploited for augmenting the AD pipelines. All artifacts needed for reproducibility purposes are provided via a GitHub repository. ( https://github.com/boschresearch/dskg-constructor — Note that we removed our internal namespaces of reused ontologies, because of confidentiality and to provide self-contained ontologies. As the original datasets are under specific licences, the KGs are not published, but we provide the scripts to generate them.)
k-Ambiguity function in the framework of offset linear canonical transform

A new version of ambiguity function (AF) associated with the offset linear canonical transform (OLCT) is considered in this paper. This new version of AF coined as the [Formula: see text]-AF associated with the OLCT ([Formula: see text]-AFOL) is defined based on the OLCT and the fractional instantaneous auto-correlation. A natural magnification effect characterized by the extra degrees of freedom of the OLCT and by a factor [Formula: see text] on the frequency axis enables the [Formula: see text]-AFOL to have flexibility to be used in cross-term reduction. Firstly, we defined the [Formula: see text]-AF associated with the OLCT ([Formula: see text]-AFOL), and establish its relationship with the [Formula: see text]-Wigner distribution in OLCT domain. Later on, we define the basic properties including the scaling, conjugate-symmetry, shifting, marginal and Moyal’s formulae of [Formula: see text]-AFOL in depth. The results show that [Formula: see text]-AFOL can be viewed as one of the generalizations of the classical AF which has elegance, simplicity and flexibility in the frequency marginal property. The novelty of our paper lies in applications part, where we have shown how the proposed transform is used for the detection of single-component and bi-component linear frequency-modulated (LFM) signals.