Reviewerly/exHarmony huggingface dataset:
Mobile-Based 3D Modeling: An In-Depth Evaluation for the Application in Indoor Scenarios

Indoor environment modeling has become a relevant topic in several application fields, including augmented, virtual, and extended reality. With the digital transformation, many industries have investigated two possibilities: generating detailed models of indoor environments, allowing viewers to navigate through them; and mapping surfaces so as to insert virtual elements into real scenes. The scope of the paper is twofold. We first review the existing state-of-the-art (SoA) of learning-based methods for 3D scene reconstruction based on structure from motion (SFM) that predict depth maps and camera poses from video streams. We then present an extensive evaluation using a recent SoA network, with particular attention on the capability of generalizing on new unseen data of indoor environments. The evaluation was conducted by using the absolute relative (AbsRel) measure of the depth map prediction as the baseline metric.
Transfer Learning Based Recurrent Neural Network Algorithm for Linguistic Analysis

Each language is a system of understanding and skills that allows language users to interact, express thoughts, hypotheses, feelings, wishes, and all that needs to be expressed. Linguistics is the research of these structures in all respects: the composition, usage, and sociology of language, in particular, are the core of linguistics. Machine Learning is the research area that allows machines to learn without being specifically scheduled. In linguistics, the design of writing is understood to be a foundation for many distinct company apps and probably the most useful if incorporated with machine learning methods. Research shows that besides text tagging and algorithm training, there are major problems in the field of Big Data. This article provides a collaborative effort (transfer learning integrated into Recurrent Neural Network) to analyze the distinct kinds of writing between the language's linear and non-computational sides, and to enhance granularity. The outcome demonstrates stronger incorporation of granularity into the language from both sides. Comparative results of machine learning algorithms are used to determine the best way to analyze and interpret the structure of the language.
STARS

Dynamic Software Product Lines (DSPLs) engineering implements self-adaptive systems by dynamically binding or unbinding features at runtime according to a feature model. However, these features may interact in unexpected and undesired ways leading to critical consequences for the DSPL. Moreover, (re)configurations may negatively affect the runtime system's architectural qualities, manifesting architectural bad smells. These issues are challenging to detect due to the combinatorial explosion of the number of interactions amongst features. As some of them may appear at runtime, we need a runtime approach to their analysis and mitigation. This thesis introduces the Behavioral Map (BM) formalism that captures information from different sources (feature model, code) to automatically detect these issues. We provide behavioral map inference algorithms. Using the Smart Home Environment (SHE) as a case study, we describe how a BM is helpful to identify critical feature interactions and architectural smells. Our preliminary results already show promising progress for both feature interactions and architectural bad smells identification at runtime.
E-governance security via public key cryptography using elliptic curve cryptography

“In the current long stretches of worldwide financial emergency the Government of the non-industrial nations like India are confronting extreme test in keeping an effective organization in its center and associated areas inside a moderate spending plan. The utilization of cutting edge Information and Communication Technology (ICT) will assist with executing the Electronic Governance which will supplant its traditional structure to convey administrations to the general population at decreased rate. To do as such, the Citizen should have a multivariate electronic instrument which will particularly distinguish the Citizen in all regard and help to speak with the Government. This paper gives a thought on E-Governance Security utilizing public key cryptography. The public key cryptography procedure called Elliptic Curve Cryptography (ECC) become famous over the most recent couple of years. The fundamental fascination of ECC is that it requires some investment challenge for an interloper to break into the cryptographic framework. ECC utilizing 160 pieces key, gives a similar security as offered by the notable RSA utilizing 1024 pieces key, accordingly prompting lower preparing time, negligible transmission capacity use and rapid exchanges.
Binary self-dual codes of various lengths with new weight enumerators from a modified bordered construction and neighbours

In this work, we define a modification of a bordered construction for self-dual codes which utilises $\lambda$-circulant matrices. We provide the necessary conditions for the construction to produce self-dual codes over finite commutative Frobenius rings of characteristic 2. Using the modified construction together with the neighbour construction, we construct many binary self-dual codes of lengths 54, 68, 82 and 94 with weight enumerators that have previously not been known to exist.
Analyzing the Potential of Zero-Shot Recognition for Document Image Classification

Document image classification is one of the most important components in business automation workflow. Therefore, a range of different supervised image classification methods have been proposed, which rely on a large amount of labeled data, which is rarely available in practice. Furthermore, retraining of these models is necessary upon the introduction of new classes. In this paper, we analyze the potential of zero-shot document image classification based on computing the agreement between the images and the textual embeddings of class names/descriptions. This enables the deployment of document image classification models without the availability of any training data at zero training cost, alongside providing seamless integration of new classes. Our results show that using zero-shot recognition achieves significantly better than chance performance on document image classification benchmarks (49.51% accuracy on Tobacco-3482 in contrast to 10% random classifier accuracy and 39.22% on RVL-CDIP dataset in contrast to 6.25% random classifier accuracy). We also show that the representation learned by a vision transformer using image-text pairs is competitive to CNNs by training a linear SVM on top of the pre-computed representations which achieves comparable performance to state-of-the-art convolutional networks (85.74% on Tobacco-3482 dataset in contrast to 71.67% from ImageNet pretrained ResNet-50). Even though the initial results look encouraging, there is still a large gap to cover for zero-shot recognition within the domain of document images in contrast to natural scene image classification, which achieves comparable performance to fully supervised baselines. Our preliminary findings pave the way for the deployment of zero-shot document image classification in production settings.
Light-field Projection for Tangible Projection Mapping

In the present study, we propose a novel projection mapping using a 3D light-field image as a light source. In recent years, spatial augmented reality has evolved into dynamic projection mapping that extends the target to moving objects. However, spatial augmented reality causes multiplexing of projection and measurement equipment, which causes various problems, such as increased psychological pressure on users and a reduced production effect. Therefore, based on the concept of stealth projection, which hides the projection device using aerial imaging technology, we propose a dynamic projection mapping method using a 3D light-field image generated in real time according to the position and orientation of the target object. As a result, a simple light-field projector consisting of an LCD panel and a lenticular lens provides projection mapping for moving objects while visually hiding the projection devices.
Modelling Short-range Quantum Teleportation for Scalable Multi-Core Quantum Computing Architectures

Multi-core quantum computing has been identified as a solution to the scalability problem of quantum computing. However, interconnecting quantum chips is not trivial, as quantum communications have their share of quantum weirdness: quantum decoherence and the no-cloning theorem makes transferring qubits a harsh challenge, where every extra nanosecond counts and retransmission is simply impossible. In this paper, we present our first steps towards thorough modeling of quantum communications for multi-core quantum computers, which may be considered as a middle point between the well-known paradigms of Quantum Internet and Network-on-Chip. In particular, we stress the deep entanglement that exists between latency and error rates in quantum computing, and how this affects the quantum network design for this scenario. Moreover, we show the concomitant trade-off between computation and communication resources for a set of parameters out of state-of-the-art experimental research. The observed behavior lets us foresee the potential of multi-core quantum architectures.
Hierarchical Lexicon Embedding Architecture for Chinese Named Entity Recognition

Named entity recognition (NER) is one of the most fundamental tasks in a variety of natural language applications. Due to the lack of delimiters in the Chinese language, Chinese NER task has been suffering from the shortage of word boundary information. Recently, incorporating word information has been proven an effective mechanism to alleviate this problem. However, how to integrate word information into the character-based model more effectively and efficiently is still a challenge. In this work, we propose a hierarchical lexicon embedding architecture for Chinese NER task. The words matched by the input sentence are divided into two categories, i.e., main words and auxiliary words, to help the model better capture useful information. In addition, the modification mainly lies in the embedding layer, as such it can be easily incorporated with different sequence modeling architectures. Experimental studies on four Chinese NER datasets have shown our method's promising potential.
Classification of the Trap-Neuter-Return Surgery Images of Stray Animals Using Yolo-Based Deep Learning Integrated with a Majority Voting System

Trap-neuter-return (TNR) has become an effective solution to reduce the prevalence of stray animals. Due to the non-culling policy for stray cats and dogs since 2017, there is a great demand for the sterilization of cats and dogs in Taiwan. In 2020, Heart of Taiwan Animal Care (HOTAC) had more than 32,000 cases of neutered cats and dogs. HOTAC needs to take pictures to record the ears and excised organs of each neutered cat or dog from different veterinary hospitals. The correctness of the archived medical photos and the different shooting and imaging angles from different veterinary hospitals must be carefully reviewed by human professionals. To reduce the cost of manual review, Yolo’s ensemble learning based on deep learning and a majority voting system can effectively identify TNR surgical images, save 80% of the labor force, and its average accuracy (mAP) exceeds 90%. The best feature extraction based on the Yolo model is Yolov4, whose mAP reaches 91.99%, and the result is integrated into the voting classification. Experimental results show that compared with the previous manual work, it can decrease the workload by more than 80%.
The Influence of Spatial Representation on Remote Peer Consultation

This paper presents a study in which we investigated the use of different devices and mechanisms to implement Mixed Reality settings for peer recommendations on furniture. In particular, we developed a mechanism to scan rooms for new furniture to be placed in, make the resulting 3D environment available to users and display virtual furniture in it. In an exploratory study, we asked 21 participants to use this environment to support an imaginary friend in their buying decision on a couch for their living room. This was done in five conditions, including a static photo of the living room, 3D representations on mobile phones and PC screens as well as mobile (cardboard-based) VR and a dedicated VR HMD. In the study, we wanted to explore how display size, 3D experience and the fidelity of HMDs affect the quality of support for recommendations by the remote peers. Our results show that the screen-based and VR-based 3D mechanisms outperform a simple photo and that VR HMDs created highest perceptions of spatial presence and were preferred by users. Surprisingly, users perceived all mechanisms showing the 3D environment to create similar qualities of support for their recommendations. We analyze these findings and derive design recommendations from them.
An Experimental Comparison of Unsupervised Keyphrase Extraction Techniques for Extracting Significant Information from Scientific Research Articles

The automatic extraction of key information from an article that expresses all of the document’s main elements is referred to as keyphrase extraction. The number of scientific research articles each year is growing. Finding a research article on relevant topics or summarizing a particular research article using important information has become time-consuming by going through the entire article. Therefore, the textual information processing task involves the automatic keyphrase extraction from a document that expresses all of the document’s main elements. This article aims to make an experimental comparison of different unsupervised keyphrase extraction approaches, namely statistical-based, graph-based, and tree-based. The experiment is conducted upon 120 research articles from different subject areas of the computer science. The comparison between different techniques is made by calculating the precision, recall, and Fl-score. The overall performance of the experimental result shows that KP-Miner, a statistical-based technique, outperforms all the other graph-based and tree-based techniques. Among the other techniques, the tree-based technique TeKET performs better after KPMiner. The statistical-based and tree-based approach performs better than the graph-based approach.
Explainable Multiple Instance Learning with Instance Selection Randomized Trees

Multiple Instance Learning (MIL) aims at extracting patterns from a collection of samples, where individual samples (called bags) are represented by a group of multiple feature vectors (called instances) instead of a single feature vector. Grouping instances into bags not only helps to formulate some learning problems more naturally, it also significantly reduces label acquisition costs as only the labels for bags are needed, not for the inner instances. However, in application domains where inference transparency is demanded, such as in network security, the sample attribution requirements are often asymmetric with respect to the training/application phase. While in the training phase it is very convenient to supply labels only for bags, in the application phase it is generally not enough to just provide decisions on the bag-level because the inferred verdicts need to be explained on the level of individual instances. Unfortunately, the majority of recent MIL classifiers does not focus on this real-world need. In this paper, we address this problem and propose a new tree-based MIL classifier able to identify instances responsible for positive bag predictions. Results from an empirical evaluation on a large-scale network security dataset also show that the classifier achieves superior performance when compared with prior art methods.
Object-Centric Representation Learning for Video Question Answering

Video question answering (Video QA) presents a powerful testbed for human-like intelligent behaviors. The task demands new capabilities to integrate video processing, language understanding, binding abstract linguistic concepts to concrete visual artifacts, and deliberative reasoning over spacetime. Neural networks offer a promising approach to reach this potential through learning from examples rather than handcrafting features and rules. However, neural networks are predominantly feature-based - they map data to unstructured vectorial representation and thus can fall into the trap of exploiting shortcuts through surface statistics instead of true systematic reasoning seen in symbolic systems. To tackle this issue, we advocate for object-centric representation as a basis for constructing spatio-temporal structures from videos, essentially bridging the semantic gap between low-level pattern recognition and high-level symbolic algebra. To this end, we propose a new query-guided representation framework to turn a video into an evolving relational graph of objects, whose features and interactions are dynamically and conditionally inferred. The object lives are then summarized into résumés, lending naturally for deliberative relational reasoning that produces an answer to the query. The framework is evaluated on major Video QA datasets, demonstrating clear benefits of the object-centric approach to video reasoning.
Deep Learning-Based Approach for Satellite Image Reconstruction Using Handcrafted Prior

We propose a randomly initialized neural network as handcrafted prior to distorted satellite image for its restoration. The model is applied for cloud removal and proved efficient. Extensive experiments on the satellite datasets demonstrate efficiency of the proposed model both quantitative and qualitative. Further, the proposed approach also removed the dependency on pre-training datasets. In our study, RGB monochromatic satellite images were considered with the obscured area of varying shapes, lying in the range of 14–30%. Reconstructed image with MSE 0.131 and PSNR of 80.937 is obtained. Another inference deduced from the results is structural symmetry index (SSIM) values are better for red and green bands when compared to blue band. Image hash value is also calculated and found satisfactory.
Design of Neural Networks

Abstract Artificial Neural Networks (ANN) and Deep Learning (DL) are used to solve complex problems including image recognition, speech recognition and have applications in new technologies for autonomous driving, facial recognition, detecting cancers from imaging samples among others. Various design considerations are involved in the design, training, and testing of Artificial Neural Networks (ANN). These include the design of the input/output layer, the structure and number of hidden layers, the data/data-structures of variables, the transformative functions embedded in the network, the optimizers being considered, the learning rate and its systematic adjustment, the prudent usage of dropout, the parallelism-related batch-size, the number of epochs, the adaptive logic for systematically changing the network for better fit, etc. While all these methods and techniques are sensible and relevant, there lacks an overarching framework for the needed design. This paper considers the design of an ANN from an Axiomatic Design (AD) perspective that parallels the biological inspiration for ANN’s in the first place, i.e., the brain. The axiomatic design approach is used for explicating and extricating the form, function, and adaptive evolution of the underlying network.
Extrinsic parameter's adjustment and potential implications in <i>Plasmodium falciparum</i> malaria diagnosis

Malaria is a major public health concern, affecting over 3.2 billion people in 91 countries. The advent of digital microscopy and Machine learning with the aim of automating Plasmodium falciparum diagnosis extensively depends on the extracted image features. The color of the cells, plasma, and stained artifacts influence the topological, geometrical, and statistical parameters being used to extract image features. During microscopic image acquisition, custom adjustments to the condenser and color temperature controls often have an influence on the extracted statistical features. But, our human visual system sub-consciously adjusts the color and retains the originality in a different lighting environment. Despite the use of appropriate image preprocessing, findings from the literature indicate that statistical feature variations exist, allowing the risk of P. falciparum misinterpretation. In order to eliminate this pervasive variation, the current work focuses on preprocessing the extracted statistical features rather than the prepossessing of the source image. It begins with the augmentation of series images for a microscopic field by inducing illumination variations during the microscopic image acquisition stage. A set of such image series is analyzed using a Nonlinear Regression Model to generalize the relationship between microscopic images acquired with variable ambient brightness and a specific feature. The projection point of the centroid feature onto the brightness parameter is identified in the model and it is denoted as the optimum brightness factor (OBF). Using the model, the feature correction factor (CF) is calculated from the rate of change of feature values over the interval OBF, and the brightness of the test image is processed. The present work has investigated OBF for selected image textural features, namely Contrast, Homogeneity, Entropy, Energy, and Correlation individually from its co-occurrence matrices. For performance analysis, the best state-of-the-art method uses selected texture as a subset feature to evaluate the effectiveness of P. falciparum malaria classification. Then, the impact of proposed feature processing is evaluated on 274 blood smear images with and without Feature Correction (FC). As a result, the "p" value is less than .05, which leads to the result that it is highly significant and the classification accuracy and F-score of P. falciparum malaria are increased.
Decoupled Representation Learning for Attributed Networks

Network representation learning or network embedding has attracted wide attention due to its effectiveness on various network-oriented applications in recent years. Though large efforts have been made, they usually model the interactions between nodes reflected by network structure and attributes in a coupled way. To this end, in this article, we comprehensively study the problem of learning attributed network embedding, which focuses on characterizing different types of interactions among nodes and alleviating the sparse attribute problem as well. Specifically, we propose a novel DeCoupled Network Embedding (DCNE) model to learn node representations in a unified framework. We first respectively project both nodes and attributes into low-dimensional vectorial space. Then, we introduce a novel ‘`decoupled-fusion’' learning process into each graph layer to iteratively generate the node embeddings. In particular, we propose two adapted graph convolution modules to decouple the learning of network structure and attributes respectively, and a fusion module to adaptively aggregate the information. Next, we adopt a modified mini-batch algorithm to iteratively aggregate the higher-order information of both nodes and attributes within a multi-task learning framework. Extensive experiments on five public datasets demonstrate that DCNE could outperform state-of-the-art methods on multiple benchmark tasks.