Reviewerly/exHarmony huggingface dataset:
Approaches of Blockchain with AI: Challenges &amp;amp; Future Direction

Artificial Intelligence and Blockchain have recently become the two hottest technology trends. Blockchain can be defined as the chain of blocks in which every block holds the record of every transaction happening among the mining nodes in a network that is not controlled by any authority. A Blockchain is a distributed ledger that ensures the security, immutability, and transparency of the stored data. Once the data gets stored in the ledger, it becomes an extremely difficult task to temper with. This technology is being widely used in many sectors like cryptocurrency, smart contracts, government, and many others. On the other hand, Artificial Intelligence (AI) is a branch of computer science that is mainly focused on creating such machines that are capable of performing various typical tasks that require human intelligence, sometimes also known as machine intelligence. Researchers have been exploring the combination of these two technologies, as the integration of these two technologies can highly develop the block chain’s underlying architecture and enhance AI's dormant.
Vessel segmentation for automatic registration of untracked laparoscopic ultrasound to CT of the liver

Registration of Laparoscopic Ultrasound (LUS) to a pre-operative scan such as Computed Tomography (CT) using blood vessel information has been proposed as a method to enable image-guidance for laparoscopic liver resection. Currently, there are solutions for this problem that can potentially enable clinical translation by bypassing the need for a manual initialisation and tracking information. However, no reliable framework for the segmentation of vessels in 2D untracked LUS images has been presented.We propose the use of 2D UNet for the segmentation of liver vessels in 2D LUS images. We integrate these results in a previously developed registration method, and show the feasibility of a fully automatic initialisation to the LUS to CT registration problem without a tracking device.We validate our segmentation using LUS data from 6 patients. We test multiple models by placing patient datasets into different combinations of training, testing and hold-out, and obtain mean Dice scores ranging from 0.543 to 0.706. Using these segmentations, we obtain registration accuracies between 6.3 and 16.6 mm in 50% of cases.We demonstrate the first instance of deep learning (DL) for the segmentation of liver vessels in LUS. Our results show the feasibility of UNet in detecting multiple vessel instances in 2D LUS images, and potentially automating a LUS to CT registration pipeline.
Local Anomaly Detection for Multivariate Time Series by Temporal Dependency Based on Poisson Model

Multivariate time series data are invasive in different domains, ranging from data center supervision and e-commerce data to financial transactions. This kind of data presents an important challenge for anomaly detection due to the temporal dependency aspect of its observations. In this article, we investigate the problem of unsupervised local anomaly detection in multivariate time series data from temporal modeling and residual analysis perspectives. The residual analysis has been shown to be effective in classical anomaly detection problems. However, it is a nontrivial task in multivariate time series as the temporal dependency between the time series observations complicates the residual modeling process. Methodologically, we propose a unified learning framework to characterize the residuals and their coherence with the temporal aspect of the whole multivariate time series. Experiments on real-world datasets are provided showing the effectiveness of the proposed algorithm.
Graph Contrastive Learning Automated

Self-supervised learning on graph-structured data has drawn recent interest for learning generalizable, transferable and robust representations from unlabeled graphs. Among many, graph contrastive learning (GraphCL) has emerged with promising representation learning performance. Unfortunately, unlike its counterpart on image data, the effectiveness of GraphCL hinges on ad-hoc data augmentations, which have to be manually picked per dataset, by either rules of thumb or trial-and-errors, owing to the diverse nature of graph data. That significantly limits the more general applicability of GraphCL. Aiming to fill in this crucial gap, this paper proposes a unified bi-level optimization framework to automatically, adaptively and dynamically select data augmentations when performing GraphCL on specific graph data. The general framework, dubbed JOint Augmentation Optimization (JOAO), is instantiated as min-max optimization. The selections of augmentations made by JOAO are shown to be in general aligned with previous best practices observed from handcrafted tuning: yet now being automated, more flexible and versatile. Moreover, we propose a new augmentation-aware projection head mechanism, which will route output features through different projection heads corresponding to different augmentations chosen at each training step. Extensive experiments demonstrate that JOAO performs on par with or sometimes better than the state-of-the-art competitors including GraphCL, on multiple graph datasets of various scales and types, yet without resorting to any laborious dataset-specific tuning on augmentation selection. We release the code at this https URL.
Effectiveness of the E-Ticket System Using QR Codes For Smart Transportation Systems

The background of this research is to know the effectiveness of using QR codes for E-ticket payments in the ticket booking system in the transportation sector, by providing easy payments to the public, it will be able to facilitate ordering and payment of tickets, therefore it will be able to increase ticket sales. The research method used is to use the literature review method by reviewing many journals and finding the latest research problems so that the research carried out becomes renewable research and can solve the research problems raised. The problem raised in this study is by analyzing the effectiveness of using QR codes, data obtained from previous research studies, by conducting literature reviews, will be able to find problems and solutions from the research raised. This research will produce a system proposal that will make it easier for the public to use the ticket payment system, the use of QR codes will simplify transactions and speed up the payment process.
Solar Irradiance Forecast Based on Cloud Movement Prediction

Solar energy production based on a photovoltaic system is closely related to solar irradiance. Therefore, the planning of production is based on the prediction of solar irradiance. The optimal use of different energy storage systems requires an accurate prediction of solar irradiation with at least an hourly time horizon. In this work, a solar irradiance prediction method is developed based on the prediction of solar shading by clouds. The method is based on determining the current cloud position and estimating the velocity from a sequence of multiple images taken with a 180-degree wide-angle camera with a resolution of 5 s. The cloud positions for the next hour interval are calculated from the estimated current cloud position and velocity. Based on the cloud position, the percentage of solar overshadowing by clouds is determined, i.e., the solar overshadowing curve for the next hour interval is calculated. The solar irradiance is determined by normalizing the percentage of the solar unshadowing curve to the mean value of the irradiance predicted by the hydrometeorological institute for that hourly interval. Image processing for cloud detection and localization is performed using a computer vision library and the Java programming language. The algorithm developed in this work leads to improved accuracy and resolution of irradiance prediction for the next hour interval. The predicted irradiance curve can be used as a predicted reference for solar energy production in energy storage system optimization.
Inferring Emotion from Large-scale Internet Voice Data: A Semi-supervised Curriculum Augmentation based Deep Learning Approach

Effective emotion inference from user queries helps to give a more personified response for Voice Dialogue Applications(VDAs). The tremendous amounts of VDA users bring in diverse emotion expressions. How to achieve a high emotion inferring performance from large-scale Internet Voice Data in VDAs? Traditionally, researches on speech emotion recognition are based on acted voice datasets, which have limited speakers but strong and clear emotion expressions. Inspired by this, in this paper, we propose a novel approach to leverage acted voice data with strong emotion expressions to enhance large-scale unlabeled internet voice data with diverse emotion expressions for emotion inferring. Specifically, we propose a novel semi-supervised multi-modal curriculum augmentation deep learning framework. First, to learn more general emotion cues, we adopt a curriculum learning based epoch-wise training strategy, which trains our model guided by strong and balanced emotion samples from acted voice data and sub-sequently leverages weak and unbalanced emotion samples from internet voice data.Second, to employ more diverse emotion expressions, we design a Multi-path Mix-match Multimodal Deep Neural Network(MMMD), which effectively learns feature representations for multiple modalities and trains labeled and unlabeled data in hybrid semi-supervised methods for superior generalization and robustness. Experiments on an internet voice dataset with 500,000 utterances show our method outperforms (+10.09% in terms of F1) several alternative baselines, while an acted corpus with 2,397 utterances contributes 4.35%. To further compare our method with state-of-the-art techniques in traditionally acted voice datasets, we also conduct experiments on public dataset IEMOCAP. The results reveal the effectiveness of the proposed approach.
Manual Evaluation Matters: Reviewing Test Protocols of Distantly Supervised Relation Extraction

Distantly supervised (DS) relation extraction (RE) has attracted much attention in the past few years as it can utilize large-scale autolabeled data.However, its evaluation has long been a problem: previous works either take costly and inconsistent methods to manually examine a small sample of model predictions, or directly test models on auto-labeled datawhich, by our check, produce as much as 53% wrong labels at the entity pair level in the popular NYT10 dataset.This problem has not only led to inaccurate evaluation, but also made it hard to understand where we are and what's left to improve in the research of DS-RE.To evaluate DS-RE models more credibly, we build manually-annotated test sets for two DS-RE datasets, NYT10 and Wiki20, and thoroughly evaluate several competitive models, especially the latest pre-trained ones.The experimental results show that the manual evaluation can indicate very different conclusions from automatic ones, especially some unexpected observations, e.g., pre-trained models can achieve dominating performance while being more susceptible to false-positives compared with previous methods.We hope that both our manual test sets and observations can help advance future DS-RE research.
Keyword-Guided Neural Conversational Model

We study the problem of imposing conversational goals/keywords on open-domain conversational agents, where the agent is required to lead the conversation to a target keyword smoothly and fast. Solving this problem enables the application of conversational agents in many real-world scenarios, e.g., recommendation and psychotherapy. The dominant paradigm for tackling this problem is to 1) train a next-turn keyword classifier, and 2) train a keyword-augmented response retrieval model. However, existing approaches in this paradigm have two limitations: 1) the training and evaluation datasets for next-turn keyword classification are directly extracted from conversations without human annotations, thus, they are noisy and have low correlation with human judgements, and 2) during keyword transition, the agents solely rely on the similarities between word embeddings to move closer to the target keyword, which may not reflect how humans converse. In this paper, we assume that human conversations are grounded on commonsense and propose a keyword-guided neural conversational model that can leverage external commonsense knowledge graphs (CKG) for both keyword transition and response retrieval. Automatic evaluations suggest that commonsense improves the performance of both next-turn keyword prediction and keyword-augmented response retrieval. In addition, both self-play and human evaluations show that our model produces responses with smoother keyword transition and reaches the target keyword faster than competitive baselines.
A Comprehensive Study of Mammogram Classification Techniques

Cancer instances have increased in the recent past and are risking many lives. Every kind of cancer is caused due to a malignant (cancerous) tumor which looks pretty similar to a benign (non-cancerous) tumor which makes it difficult to distinguish from one another. Computer-Aided Diagnosis (CAD) is very useful in the early detection of a tumor from its development stage. Many techniques are available in the literature for lesion or mass classification but the challenges faced to train a model are also of great concern. There comes a question about which method to implement for early detection of cancer. In this paper, we have discussed various classification techniques that are categorized based on function, probability, rule and similarity. Analysis of these methods, their drawbacks, and challenges are also discussed. Comparative analysis of existing approaches used to classify mammograms is also presented in the paper. The challenges to train a model also have come from the type of dataset used for classification process. Sometimes the dataset has many anomalies like redundancy, variable size and inconsistency in dimensions that cannot be negotiated. Sometimes the model (architecture) we chose for the training purpose has extensive computation, making it inefficient. These challenges have to be solved during the preprocessing and training phase of a model. This paper mainly focused at various challenges related to mammogram datasets and classification techniques. Methods to handle these challenges are also discussed in the paper.
A Fast Bi-Directional A* Algorithm Based on Quad-Tree Decomposition and Hierarchical Map

Although the popular path-planning algorithms based on graph-search such as Dijkstra and A* guarantee to find the optimal path, the search time will increase rapidly with the growth of map size. Those traditional graph-search based algorithms ignore the distribution of obstacles, causing much time wasted on exploring unrelated regions. A fast bidirectional-A* algorithm based on hierarchical map(QH-A*) was proposed in this paper with high real-time performance. QH-A* consists of two parts: offline pre-processing and online search. The offline pre-processing of QH-A* comprises map decomposition and construction of hierarchical map. In the map decomposition step, a modified quad-tree decomposition method was used to recursively divide the initial map into different regions. Then boundary nodes of each region were extracted. During the construction of hierarchical map, a kind of hierarchical map was defined and precomputation was done to construct the hierarchical map. In the online-search step, a revised bidirectional-A* specific to hierarchical map was proposed to finish the path-planning task. Experiments and theories proved that QH-A* can always find the shortest path. In addition, the search area required by QH-A* was 70%-80% less than that of A*, which verified high real-time performance of QH-A*.
Artificial Intelligence in the modern educational space: problems and prospects

The article discusses the concept of "artificial intelligence", as well as its main characteristics. Shows the features and examples of the introduction of artificial intelligence technologies into the modern foreign and domestic educational space. Listes the problems and prospects of their application in the field of modern education.
Binary Neural Network Hashing for Image Retrieval

Hashing has become increasingly important for large-scale image retrieval, of which the low storage cost and fast searching are two key properties. However, existing methods adopt large neural networks, which are hard to be deployed in resource-limited devices due to the unacceptable memory and runtime overhead. We address that this huge overhead of neural networks somewhatviolates the appealing properties of hashing. In this paper, we propose a novel deep hashing method, called Binary Neural Network Hashing (BNNH) for fast image retrieval. Specifically, we construct an efficient binarized network architecture to provide lighter model and faster inference, which directly generates binary outputs as the desired hash codes without introducing the quantization loss. Besides, in order to circumvent the huge performance degradation caused by the extremely quantized activations, we introduce a simple yet effective activation-aware loss to explicitly guide the updating of activations in intermediate layers. Extensive experiments conducted on three benchmarks show that the proposed method outperforms the state-of-the-art binarization methods by large margins and validate the efficiency of BNNH.
Realization of nonadiabatic holonomic multiqubit controlled gates with Rydberg atoms

Nonadiabatic holonomic gates provide an effective means to perform high-speed and error-resilient manipulation of quantum states. It is practically important to realize nonadiabatic holonomic multiqubit controlled gates since multiqubit controlled gates are widely used in quantum information processing. Rydberg atoms are an appealing physical system for the realization of nonadiabatic holonomic multiqubit controlled gates as the Rydberg-mediated interaction is beneficial to couple two qubits. In this paper, we put forward a scheme for the realization of nonadiabatic holonomic multiqubit controlled gates based on Rydberg atoms, where an $(n+1)$-qubit controlled-$(\mathrm{n}\ifmmode\cdot\else\textperiodcentered\fi{}\mathbf{\ensuremath{\sigma}})$ gate can be realized by $(2n\ensuremath{-}1)$ basic operations. Moreover, the effective coupling between two qubits is in the first-order strength of Rabi frequencies, which allows for the implementation of quantum gates within a short duration.
Open Set Domain Adaptation With Soft Unknown-Class Rejection

The goal of domain adaptation (DA) is to train a good model for a target domain, with a large amount of labeled data in a source domain but only limited labeled data in the target domain. Conventional closed set domain adaptation (CSDA) assumes source and target label spaces are the same. However, this is not quite practical in real-world applications. In this work, we study the problem of open set domain adaptation (OSDA), which only requires the target label space to partially overlap with the source label space. Consequently, the solution to OSDA requires unknown classes detection and separation, which is normally achieved by introducing a threshold for the prediction of target unknown classes; however, the performance can be quite sensitive to that threshold. In this article, we tackle the above issues by proposing a novel OSDA method to perform soft rejection of unknown target classes and simultaneously match the source and target domains. Extensive experiments on three standard datasets validate the effectiveness of the proposed method over the state-of-the-art competitors.
Federated Learning with Dynamic Transformer for Text to Speech

Text to speech (TTS) is a crucial task for user interaction, but TTS model training relies on a sizable set of high-quality original datasets. Due to privacy and security issues, the original datasets are usually unavailable directly. Recently, federated learning proposes a popular distributed machine learning paradigm with an enhanced privacy protection mechanism. It offers a practical and secure framework for data owners to collaborate with others, thus obtaining a better global model trained on the larger dataset. However, due to the high complexity of transformer models, the convergence process becomes slow and unstable in the federated learning setting. Besides, the transformer model trained in federated learning is costly communication and limited computational speed on clients, impeding its popularity. To deal with these challenges, we propose the federated dynamic transformer. On the one hand, the performance is greatly improved comparing with the federated transformer, approaching centralize-trained Transformer-TTS when increasing clients number. On the other hand, it achieves faster and more stable convergence in the training phase and significantly reduces communication time. Experiments on the LJSpeech dataset also strongly prove our method's advantage.
Intent Detection and Slot Filling for Vietnamese

Intent detection and slot filling are important tasks in spoken and natural language understanding. However, Vietnamese is a low-resource language in these research topics. In this paper, we present the first public intent detection and slot filling dataset for Vietnamese. In addition, we also propose a joint model for intent detection and slot filling, that extends the recent state-of-the-art JointBERT+CRF model with an intent-slot attention layer to explicitly incorporate intent context information into slot filling via soft intent label embedding. Experimental results on our Vietnamese dataset show that our proposed model significantly outperforms JointBERT+CRF. We publicly release our dataset and the implementation of our model at: this https URL
A Smart Adversarial Attack on Deep Hashing Based Image Retrieval

Deep hashing based retrieval models have been widely used in large-scale image retrieval systems. Recently, there has been a surging interest in studying the adversarial attack problem in deep hashing based retrieval models. However, the effectiveness of existing adversarial attacks is limited by their poor perturbation management, unawareness of ranking weight, and only laser-focusing on the attack image. These shortages lead to high perturbation costs yet low AP reductions. To overcome these shortages, we propose a novel adversarial attack framework to improve the effectiveness of adversarial attacks. Our attack designs a dimension-wise surrogate Hamming distance function to help with wiser perturbation management. Further, in generating adversarial examples, instead of focusing on a single image, we propose to collectively incorporate relevant images combined with an AP-oriented (average precision) weight function. In addition, our attack can deal with both untargeted and targeted adversarial attacks in a flexible manner. Extensive experiments demonstrate that, with the same attack performance, our model significantly outperforms state-of-the-art models in perturbation cost on both untargeted and targeted attack tasks.