Reviewerly/exHarmony huggingface dataset:
Dual-Stream Contrastive Learning for Compositional Zero-Shot Recognition

Compositional Zero-Shot learning (CZSL) requires recognizing unseen attribute-object compositions using observed visual primitives attributes and objects in a training set, which is a critical capacity for learning systems because the long tail of new combinations dominates the distribution in the real world. However, CZSL is a challenging problem because learning systems tend to learn the dependencies between objects and attributes, which is not conducive to composition classification, and incorrect dependencies will mislead the classification of new combinations of known attributes and objects. This paper primarily introduces a novel yet effective dual-stream contrastive learning method with two main objectives: making the learned representations discriminative and transferring knowledge more efficiently from seen to unseen compositions. Specifically, we generate positive and negative pairs based on the similarity of different concepts (attributes and objects), independently capturing the discriminative representations of concepts. Meanwhile, unlike existing contrastive methods that select negative samples randomly, we construct confusable compositional representations as the negatives to explore the intrinsic relevance between attributes and objects, which can improve the generalization from seen to unseen compositions. Experimental results on two benchmarks show that the proposed method outperforms the state-of-the-arts.
Learning Traces by Yourself: Blind Image Forgery Localization via Anomaly Detection With ViT-VAE

Most of existing deep learning models for image forgery localization rely on a large number of high-quality labeled samples for training. The training procedures are performed off-line and without adaptation to the image under scrutiny. In this letter, we propose to perform run-time learning of the forgery traces from the suspicious image itself. To this aim, a Variational Auto-Encoder (VAE) model is learned to reconstruct small cliques of the suspicious image, and those cliques with anomalous larger reconstruction errors are therein identified as forged. To further enhance performance, Vision Transform (ViT) is employed as the VAE encoder, and multi-modal input information is explored by considering noise inconsistency, high-pass residual inconsistency, and edge discontinuity. Evaluation on widely used benchmark datasets shows our method outperforms existing blind methods by a large margin, and is competitive against approaches that use ground-truth for supervised training.
Exploring the Potential of BERT-BiLSTM-CRF and the Attention Mechanism in Building a Tourism Knowledge Graph

As an important infrastructure in the era of big data, the knowledge graph can integrate and manage data resources. Therefore, the construction of tourism knowledge graphs with wide coverage and of high quality in terms of information from the perspective of tourists’ needs is an effective solution to the problem of information clutter in the tourism field. This paper first analyzes the current state of domestic and international research on constructing tourism knowledge graphs and highlights the problems associated with constructing knowledge graphs, which are that they are time-consuming, laborious and have a single function. In order to make up for these shortcomings, this paper proposes a set of systematic methods to build a tourism knowledge graph. This method integrates the BiLSTM and BERT models and combines these with the attention mechanism. The steps of this methods are as follows: First, data preprocessing is carried out by word segmentation and removing stop words; second, after extracting the features and vectorization of the words, the cosine similarity method is used to classify the tourism text, with the text classification based on naive Bayes being compared through experiments; third, the popular tourism words are obtained through the popularity analysis model. This paper proposes two models to obtain popular words: One is a multi-dimensional tourism product popularity analysis model based on principal component analysis; the other is a popularity analysis model based on emotion analysis; fourth, this paper uses the BiLSTM-CRF model to identify entities and the cosine similarity method to predict the relationship between entities so as to extract high-quality tourism knowledge triplets. In order to improve the effect of entity recognition, this paper proposes entity recognition based on the BiLSTM-LPT and BiLSTM-Hanlp models. The experimental results show that the model can effectively improve the efficiency of entity recognition; finally, a high-quality tourism knowledge was imported into the Neo4j graphic database to build a tourism knowledge graph.
Learning to Distill Graph Neural Networks

Graph Neural Networks (GNNs) can effectively capture both the topology and attribute information of a graph, and have been extensively studied in many domains. Recently, there is an emerging trend that equips GNNs with knowledge distillation for better efficiency or effectiveness. However, to the best of our knowledge, existing knowledge distillation methods applied on GNNs all employed predefined distillation processes, which are controlled by several hyper-parameters without any supervision from the performance of distilled models. Such isolation between distillation and evaluation would lead to suboptimal results. In this work, we aim to propose a general knowledge distillation framework that can be applied on any pretrained GNN models to further improve their performance. To address the isolation problem, we propose to parameterize and learn distillation processes suitable for distilling GNNs. Specifically, instead of introducing a unified temperature hyper-parameter as most previous work did, we will learn node-specific distillation temperatures towards better performance of distilled models. We first parameterize each node's temperature by a function of its neighborhood's encodings and predictions, and then design a novel iterative learning process for model distilling and temperature learning. We also introduce a scalable variant of our method to accelerate model training. Experimental results on five benchmark datasets show that our proposed framework can be applied on five popular GNN models and consistently improve their prediction accuracies with 3.12% relative enhancement on average. Besides, the scalable variant enables 8 times faster training speed at the cost of 1% prediction accuracy.
CNN-OLSTM: Convolutional Neural Network with Optimized Long Short-Term Memory Model for Twitter based Sentiment Analysis

Social Networking continues the growth of web users, with people sharing their ideas and opinions daily in the form of texts, pictures, videos, and speech. Text classification is still an important issue because these large texts were derived from diverse sources and different thinking people. The shared concept must be incomplete, random, noisy, and in the form of different languages. To solve this issue, in this paper, Convolutional Neural Network with Optimized Long Short Term Memory Model (CNN-OLSTM) based sentimental analysis is proposed. The presented approach contains pre-processing, word2vec conversion, and prediction. Initially, data are pre-processed by using tokenization, stop word removal, and stemming. After the pre-processing, the skip-gram model (SGM) based word2vec conversion is performed. Then, the extracted vectors are given to the CNN-OLSTM classifier to classify a tweet as positive or negative polarity. In this, the CNN model effectively reduces the dimension of the input vector using max-pooling layers and convolutional layers. Also, the LSTM model is capable of catching long-term dependencies between word sequences. To enhance the LSTM performance, the rain optimization algorithm (ROA) is effectively used. The results show that the suggested CNN-OLSTM approach outperforms conventional deep neural networks in terms of accuracy.
Discrete cosine transform features matching-based forgery mask detection for copy-move forged images

In this age of digital technology, it is relatively simple to manipulate images. It is quite difficult to recognize altered photographs with the human eye, and as a result, we are frequently fooled or misled on social media platforms as a result of altered images being shared. As a result, it is of the utmost need to work on methods that are both quicker and lighter in order to categorize and pinpoint tampered or faked images. In this article, we demonstrated a methodology for detecting copy-move forging masks that uses a modified version of the discrete cosine transform (DCT) block level features extraction method. The altered photos have their feature information retrieved at the block level. a procedure in which blocks of the proper size are chosen experimentally. In addition, these block characteristics are compared by measuring the lexicographic distance between the feature vectors in order to find blocks that are similar to one another. The visual findings are offered for comparison between the real mask and the one that was anticipated. The detection accuracy of fake masks is around 95% on average.
Articulated 3D Human-Object Interactions From RGB Videos: An Empirical Analysis of Approaches and Challenges

Human-object interactions with articulated objects are common in everyday life. Despite much progress in single-view 3D reconstruction, it is still challenging to infer an articulated 3D object model from an RGB video showing a person manipulating the object. We canonicalize the task of articulated 3D human-object interaction reconstruction from RGB video, and carry out a systematic benchmark of five families of methods for this task: 3D plane estimation, 3D cuboid estimation, CAD model fitting, implicit field fitting, and free-form mesh fitting. Our experiments show that all methods struggle to obtain high accuracy results even when provided ground truth information about the observed objects. We identify key factors which make the task challenging and suggest directions for future work on this challenging 3D computer vision task.
Towards Complete and Scalable Emulation of Quantum Algorithms on High-Performance Reconfigurable Computers

Contemporary quantum computers face many critical challenges that limit their usefulness for practical applications. A primary limiting factor is classical-to-quantum (C2Q) data encoding, which requires specific circuits for quantum state initialization. The required state initialization circuits are often complex and violate decoherence constraints, particularly for I/O intensive applications. Existing Noisy Intermediate-Scale Quantum (NISQ) devices are noise-sensitive and have low quantum bit (qubit) counts, thus limiting the applicability of C2Q circuits for encoding large and realistic datasets. This has made the study of complete and realistic circuits that include data encoding challenging and has also led to a heavy dependency on costly and resource-intensive simulations on classical platforms. In this work, we propose a cost-effective, classical-hardware-accelerated framework for realistic and complete emulation of quantum algorithms. The emulation framework incorporates components for the critical C2Q data encoding process, as well as architectures for quantum algorithms such as the quantum Haar transform (QHT). The framework is used to investigate optimizations for C2Q and QHT algorithms, and the corresponding optimized quantum circuits are presented. The framework is implemented on a High-Performance Reconfigurable Computer (HPRC) which emulates the proposed QHT circuits combined with proposed C2Q data encoding methods. For performance benchmarks, CPU-based emulations and simulations on a state-of-the-art quantum computing simulator are also carried out. Results show that the proposed hardware-accelerated emulation framework is more efficient in terms of speed and scalability compared to CPU-based emulation and simulation.
COLLABORATIVE AUGMENTED REALITY - A SURVEY

In the past few years, technology breakthroughs have made Augmented Reality (AR) applications more user friendly and accessible than ever. New AR technologies show great promise and potential to influence and enhance the activities related to a wide range of areas, such as education, military industry, entertainment, sports or medicine and surgical training. In complementarity with Virtual Reality, AR is now considered to be the future of human-computer interaction, by revolutionizing the way information is presented and transforming the surroundings into one broad user interface. In particular, AR systems will soon play an important role in communication and collaboration, creating innovative collaborative environments that allow multiple users to interact simultaneously. Hybrid interfaces that combine AR technology with existing telecommunication techniques provide immense power and freedom in multi-participant settings where the physical and virtual worlds blend perfectly and form a mixed reality. Their main objectives are to improve the cooperation through augmented face-to-face interactions, to enrich the remote collaboration by virtually co-locating the participants and to augment physical spaces with new modalities of interaction between people. Using latest AR technologies, applications can provide unique collaborative experiences for all domains, supporting new means of communication and socialization and making working together very effective. This paper aims to present a survey on collaborative augmented reality and its potential in various contexts. It thoroughly reviews existing collaborative AR systems in areas like those mentioned above, identifying their accomplishments as well as limitations, and highlighting the most recent discoveries and future trends in the field.
Robust Initial Orbit Determination for Surveillance Doppler-Only Radars

An initial orbit determination algorithm for surveillance Doppler-only radars with embedded quantification capabilities is presented. The method is based on a combination of Gauss' and Lambert's solvers formulated in the differential algebra framework, which provides the Taylor expansion of the state estimate with respect to the measurement uncertainties. This feature makes the approach particularly suited for handling data association problems. The first part of the paper describes the mathematical formulation of the method, while an extensive analysis of its performance and a comparison with a reference algorithm are carried out in the second part, using both simulated and real data. The proposed approach is shown to be accurate and robust and particularly suited for short-arc observations.
CEO-MLCPP: Control-Efficient and Obstacle-Aware Multi-Layer Coverage Path Planner for 3D Reconstruction with UAVs

In this paper, we propose a novel model-based coverage path planner for the 3D reconstruction of a target structure with an unmanned aerial vehicle (UAV). The proposed method rapidly calculates initial viewpoints considering the ground sampling distance (GSD) by partitioning a structure by height. Then, optimal viewpoints are selected by checking the collision and calculating overlaps and coverage. Next, the newly developed collision-aware Traveling Salesman Problem (CTSP) is used to connect the optimal viewpoints while guaranteeing the shortest distance and obstacle avoidance. Finally, the resulting path is refined as a control-efficient trajectory that considers the dynamics of UAVs. The performance of the proposed algorithm is verified by experiments on diverse structures.
A vast dataset for Kurdish handwritten digits and isolated characters recognition

This article presents two massive datasets for central Kurdish handwriting digits and isolated characters named K-ZHMARA and K-PIT. The first dataset, named K-ZHMARA dataset, contains 70,000 images of Kurdish digits, 7000 images for each digit, and a printed A4 paper with a grid of 10 × 10 is used for data collection. Apart from digits, the K-PIT dataset includes 245,000 images of all Kurdish characters, 7000 images for each character; data was collected via a printed A4 paper with a grid of 12 × 10 for this dataset. Moreover, both datasets include 315,000 images. Python programming has been used to scan each piece of paper, segment, crop, resize, binarize, and invert the images via edge detection and image processing techniques.
Exemplar Free Class Agnostic Counting

We tackle the task of Class Agnostic Counting, which aims to count objects in a novel object category at test time without any access to labeled training data for that category. All previous class agnostic counting methods cannot work in a fully automated setting, and require computationally expensive test time adaptation. To address these challenges, we propose a visual counter which operates in a fully automated setting and does not require any test time adaptation. Our proposed approach first identifies exemplars from repeating objects in an image, and then counts the repeating objects. We propose a novel region proposal network for identifying the exemplars. After identifying the exemplars, we obtain the corresponding count by using a density estimation based Visual Counter. We evaluate our proposed approach on FSC-147 dataset, and show that it achieves superior performance compared to the existing approaches. Our code and models are available at: https://github.com/Viresh-R/ExemplarFreeCounting.git .
Self-tuning and approximation via RBF neural networks

Radial Basis Function (RBF) is fit for real-time control task owing to its fast speed of learning by avoiding the local minimum problem. Moreover, RBF network can ensure the accuracy in control system owing to its robustness and adaptability. Up to now, the characters of RBF still need to be studied on how to improve the performance. In the paper, fuzzy RBF neural network and the combination with genetic algorithm are studied and simulated. In addition, RBF network is applied in self-tuning control system and validated
Can neural networks extrapolate? Discussion of a theorem by Pedro Domingos

Neural networks trained on large datasets by minimizing a loss have become the state-of-the-art approach for resolving data science problems, particularly in computer vision, image processing and natural language processing. In spite of their striking results, our theoretical understanding about how neural networks operate is limited. In particular, what are the extrapolation capabilities of trained neural networks if any? In this paper we discuss a theorem of Domingos stating that “every machine learned by continuous gradient descent is approximately a kernel machine”. According to Domingos, this fact leads to conclude that all machines trained on data are mere kernel machines. We first extend Domingo’s result in the discrete case and to networks with vector-valued output. We then study its relevance and significance on simple examples. We find that in simple cases, the “neural tangent kernel” arising in Domingos’ theorem does provide understanding of the networks’ predictions. When the task given to the network grows in complexity, the interpolation capability of the network can be effectively explained by Domingos’ theorem, and no extrapolation capability of the network beyond its learning domain is found, even when the network’s structure would allow for it. We illustrate this fact on a classic perception theory problem: recovering a shape from its boundary.
Enhanced Open and Distance Learning Using an Artificial Intelligence (AI)-Powered Chatbot: a Conceptual Framework

The data gleaned from the National Open University of Nigeria's (NOUN) E-ticketing system was studied in this paper. NOUN is one of the Open and Distance Learning (ODL) institutions, where students and their facilitators are in different physical locations. Multinomial Naive Bayes algorithm, preferred using “intent” for its classification method for the chatbot system. Within 4 months of the launch of the NOUN E-ticketing system, 38,263 tickets (students' complaints and inquiries) were generated and 30,601 have been manually responded to and closed while the remaining 7,662 tickets are still in progress. The chatbot's goal is to respond to students inquiries quickly and efficiently while easing the burden on the management system. With the availability of chatbot, students' responses will be automated and accessible 24/7. The NOUN chatbot will increase student engagement, strengthen communication and create a seamless interaction for both the ODL institutions and its students all together culminating to a robust congenial student-ODL relationship ultimately leading to a higher attraction rate and more importantly, a lower attrition rate, not just in ODL institutions alone, but to other conventional higher institutions.
Verifying Feedforward Neural Networks for Classification in Isabelle/HOL

Neural networks are being used successfully to solve classification problems, e.g., for detecting objects in images. It is well known that neural networks are susceptible if small changes applied to their input result in misclassification. Situations in which such a slight input change, often hardly noticeable by a human expert, results in a misclassification are called adversarial examples. If such inputs are used for adversarial attacks, they can be life-threatening if, for example, they occur in image classification systems used in autonomous cars or medical diagnosis. Systems employing neural networks, e.g., for safety or security-critical functionality, are a particular challenge for formal verification, which usually expects a formal specification (e.g., given as source code in a programming language for which a formal semantics exists). Such a formal specification does, per se, not exist for neural networks. In this paper, we address this challenge by presenting a formal embedding of feedforward neural networks into Isabelle/HOL and discussing desirable properties for neural networks in critical applications. Our Isabelle-based prototype can import neural networks trained in TensorFlow, and we demonstrate our approach using a neural network trained for the classification of digits on a dot-matrix display.
Correlated Two‐Photon Scattering in a 1D Waveguide Coupled to an N‐Type Four‐Level Emitter

Abstract Correlated two‐photon scattering in a waveguide quantum electrodynamics system consisting of a four‐level ‐type emitter (4LE) sidely coupled to a one‐dimensional (1D) waveguide is studied. In the two‐photon regime, scattering eigenstates of the system are constructed by imposing an open boundary condition and defining the incident state as a free plane wave, which includes a photon–photon bound state that passes through the 4LE as a composite single particle. In the multilevel system, the bound state can be tuned by the interference to generate controllable types of interactions (attractive or repulsive) and interaction strengths. Photon‐induced tunneling (bunching) in transmission and photon blockade (antibunching) in reflection are found in the system. In addition, the scattering photon pair off the system is strongly entangled in momentum space.