Reviewerly/exHarmony huggingface dataset:
Semantic-Visual Combination Propagation Network for Zero-Shot Learning

The goal of Zero-shot Learning (ZSL) is to discriminate images from unseen classes by modelling the embedding relationship between visual and semantic features. However, most existing methods ignore a fact that the hand-crafted semantic feature is generally less discriminative than the informative knowledge of visual features and therefore fail to effectively capture the semantic and visual embedding relationship among different categories. In this brief, to model the knowledge transfer among classes, we incorporate the semantic-visual Combination Propagation Network (CPN) into ZSL. Specifically, CPN consists of two graphs constructed by semantic and visual representations respectively. Through the combination of abundant semantic and visual information, CPN can learn enhanced semantic representations for ZSL classification. Furthermore, we employ an auto-encoder module to project the learned semantic features back to the original semantic features to capture complementary information. Extensive experiments on benchmark datasets demonstrate the effectiveness of CPN.
An Unsupervised Noisy Sample Detection Method for Deep Learning-Based Health Status Prediction

Due to rough environment or data-acquisition equipment failure, the machine monitoring data may be contaminated with noise, which may lead to the generation of noisy samples. For the widely used deep learning-based prediction model, some noisy samples (with low-intensity noise) may improve model generalization while some noisy samples (with high-intensity noise) may hamper the prediction models. To detect the noisy samples that hamper the prediction models, an unsupervised noisy sample detection method is proposed, thereby promoting the accuracy of the deep learning-based prediction model. First, the transforming model with an attention mechanism is built to transform the multivariate input sequences of samples to attention weight vectors, which are related to the prediction task. Then, a clustering method without hyperparameters is proposed to cluster these attention weight vectors and the hierarchy of clusters is created. Afterward, the noisy samples that hamper the prediction models are detected and removed based on the clustering thresholds, and a prediction model with higher prediction accuracy can thus be obtained. The effectiveness of the proposed method is verified using a simulated turbofan engine degradation dataset and a real milling machine monitoring dataset. The results show that the prediction errors for the two datasets have been reduced by 13.44&#x0025; and 12.97&#x0025; on average after removing the noisy samples detected by the proposed method. The comparison results show that the proposed method outperforms other methods in terms of improving the prediction accuracy.
Multi-label Aerial Image Classification via Adjacency-Based Label and Feature Co-embedding

Multi-label image classification is a fundamental task in aerial image processing, which automatically generates image annotations for better image content interpretation. Many existing methods realize multi-label classification through an image level, while they ignore the dependencies among labels and the cross-modal relations between labels and image features. In this paper, we propose a simple and intuitive multi-label classification method via adjacency-based label and feature co-embedding for aerial images. To be specific, we introduce an adjacency-based label embedding module to maintain the original label relationships in the semantic space. A label and feature co-embedding module is designed to enhance the text-image cross-modal interactions and to obtain the attention-based label-specific vectors, which effectively excavate the response relations between labels and images. Experiments on two benchmark aerial image multi-label datasets show that our approach achieves considerable performance compared with seven previous approaches. Besides, visualization analyses indicate the label embeddings learned by our model maintain a meaningful semantic topology, which explicitly exploit label-feature dependencies.
Demographic Filtering for Movie Recommendation System Using Machine Learning

In e-commerce, a film advisory scheme aims to tailor the user interface by showing things that the user will most likely be interested in based on related items that the user was interested in or items similar to users. Two facets and approaches of online recommendation systems, using the consumer and collective filtering frameworks, are the primary subject of our project. To complete the assessment, a film advisory system is developed and intended for a forecast user-backed ranking from users of related films, then evaluate and compare various algorithms and prediction models to construct the recommendation system. The film rating system often produces predicted user-based outcomes. To determine the accuracy, we take advantage of the knowledge from Movie Lens (ML) Web sites. Following research, the item-based root medium square derivation (RMSD) filtering was observed as opposed to the user-based assessment of filtering efficiency determined by an average bug between the rating and the rating expected. Our work finds the possibility for small-to-medium (SME) e-commerce and information filtering company to refine and scan through online businesses for new goods, so that demands can be satisfied and more sales can be created. Building on an algorithm to evaluate user liking, discouragement, and related customer tastes in order to produce the best outcomes with their goods or services is one of the most common methods for companies to help consumers communicate online or find objects. Including content, games, and collaborative, there are several types of suggestions framework models. By dynamic means, we mean a model that runs on the database's Web business and offers ratings or estimates at a future period that helps and maintains the interaction of consumers with the pages and applications. The explicit data input is used to form a model of choice for the buyers. Such considerations such as purchasing experience and search history should be considered. However, the five-star computer ranking datasets will be used for review of the recommendation framework in our work. The principal problems are enhancing filtering scalability, sparsity, and efficiency. In our research, we solve these problems by using algorithms based on user and material for the recommendation systems for different approaches, as we explored with users or item-based ranking estimation on ML datasets using filter users and item-based techniques.
Emotion-Type-Based Global Attention Neural Network for Emotion-Cause Pair Extraction

Emotion-Cause Pair Extraction (ECPE) aims to extract all potential pairs of emotions and related causes from a document. Previous works mainly focus on the interaction between ECPE and multiple subtasks. However, most proposed methods ignore that the emotion-cause pair (EC-pair) is a special causal relationship with an obvious emotion type. Additionally, all clauses in a document are related and EC-pair should be established in a specific context. To this end, we propose an emotion-type-based global attention neural network, called ECPE-GE, that captures document-level global information and takes advantage of the consistency of emotion type between emotion clause and EC-pair to address the ECPE task. The experimental results on a benchmark emotion cause corpus illustrate the effectiveness of our method compared with several baselines.
QS-Hyper: A Quality-Sensitive Hyper Network for the No-Reference Image Quality Assessment

Blind/no-reference image quality assessment (IQA) aims to provide a quality score for a single image without references. In this context, deep learning models can capture various image artifacts, which made significant progress in this study. However, current IQA methods generally utilize the pre-trained convolution neural networks (CNNs) on classification tasks to obtain image representations, which do not perfectly represent the quality of images. In order to solve this problem, this paper uses semi-supervised representation learning to train a quality-sensitive encoder (QS-encoder), which can extract image features specifically for image quality. Intuitively, this feature is more conducive to train the IQA model than the feature used for classification tasks. Thus, QS-encoder is plunged into a carefully designed hyper network to build a quality-sensitive hyper network (QS-hyper) to solve IQA tasks in more general and complex environments. Extensive experiments on the public IQA datasets show that our method outperformed most state-of-art methods on both Pearson linear correlation coefficient (PLCC) and Spearman’s rank correlation coefficient (SRCC), and it made 3% PLCC improvement and 3.9% SRCC improvement on TID2013 datasets. Therefore, it proves that our method is superior in capturing various image distortions, which meets a broader range of evaluation requirements.
A Model-Based Exploration Policy in Deep Q-Network

Reinforcement learning has successfully been used in many applications and achieved prodigious performance (such as video games), and DQN is a well-known algorithm in RL. However, there are some disadvantages in practical applications, and the exploration and exploitation dilemma is one of them. To solve this problem, common strategies about exploration like ɛ–greedy have risen. Unfortunately, there are sample inefficient and ineffective because of the uncertainty of later exploration. In this paper, we propose a model-based exploration method that learns the state transition model to explore. Using the training rules of machine learning, we can train the state transition model networks to improve exploration efficiency and sample efficiency. We compare our algorithm with ɛ–greedy on the Deep Q-Networks (DQN) algorithm and apply it to the Atari 2600 games. Our algorithm outperforms the decaying ɛ–greedy strategy when we evaluate our algorithm across 14 Atari games in the Arcade Learning Environment (ALE).
Hydrogeological-Mathematical Model of Formation and Management of Resources and Quality of Fresh Underground Water of the Karakalpak Artesian Basin

This article discusses the problems of using groundwater resources of the Karakalpak artesian basin. The task of drawing up a geological and mathematical model of the operational resources of groundwater in the Karakalpak artesian basin for the rational use of fresh groundwater in the basin is set. This article discusses the problem of lack of fresh groundwater in the territory of the Republic of Karakalpakstan. A method of hydrogeological-mathematical model of exploitable groundwater resources for the correct use of fresh groundwater is proposed.
Decoupling Deep Domain Adaptation Method for Class-imbalanced Learning with Domain Discrepancy

In a wide range of classification tasks, training data will produce class-imbalance due to collection difficulties in some classes, which leads to prediction biases on minority classes. For the class-imbalanced problem, existing researches are usually based on the assumption that the training dataset and the test dataset are from similar distributions. In reality, both of the datasets often come from domains with different distributions, which challenges generalization performances of models. In this paper, a decoupling deep domain adaptation method is proposed to overcome these problems. Based on the adversarial domain adaptation model, the method uses a two-stage training strategy which decouples representation learning and classifier adjustment. The results of experiments under scenarios of bearing fault diagnosis and digit images classification with class-imbalance and domain discrepancy show that the effect of the combination of domain adaptation method and specific decoupling strategy is better than that of one-stage training only using resampling or cost-sensitive methods in the domain adaptation model.
Intelligent pharmaceutical patent search on a near-term gate-based quantum computer

Abstract Pharmaceutical patent analysis is the key to product protection for pharmaceutical companies. In patent claims, a Markush structure is a standard chemical structure drawing with variable substituents. Overlaps between apparently dissimilar Markush structures are nearly unrecognizable when the structures span a broad chemical space. We propose a quantum search-based method which performs an exact comparison between two non-enumerated Markush structures with a constraint satisfaction oracle. The quantum circuit is verified with a quantum simulator and the real effect of noise is estimated using a five-qubit superconductivity-based IBM quantum computer. The possibilities of measuring the correct states can be increased by improving the connectivity of the most computation intensive qubits. Depolarizing error is the most influential error. The quantum method to exactly compares two patents is hard to simulate classically and thus creates a quantum advantage in patent analysis.
MH Pose: 3D Human Pose Estimation based on High-quality Heatmap

Human pose estimation is a key technology in the field of human action recognition. It aims at recognizing human poses by extracting features in images or videos. At present, human body occlusion in multi-camera views is a major challenge for human body pose estimation. To tackle the problem, we proposed the MaxHeatmap Pose model. The model obtains high-quality heatmap estimation through the MH structure module, in order to locate the target person more accurately. Then the HumanPose regression network is proposed to estimate the detailed 3D human pose. The experimental results on the data set show that the performance of the model is better than previous methods.
The Analysis of The Codes in The Textual Steganography Technologies

The methods of reliable protection from tampered access were always of current problems, but it still hasn’t been cleared up yet. One of the main goals of the protection of information takes special encoding of the textual informational resource for transferring the specific information. Using the principle of cryptography we can protect the information but by this, we show how important the resource is. Because of that, it’s more logical to hide such a piece of information. For this, we’ll be using steganography - it is transferring the information by concealing it in the exact text, image, audio, or video format. Likewise, we need to remember that a large quantity of information is placed in text form: books, articles, electronic correspondence, documents, reports, and much more besides.In this article were reviewed and analyzed methods of textual steganography, their principles, and characteristics. Were picked out the most optimal methods of textual information.
Towards Goal-Oriented Software Requirements Elicitation

Correct and unambiguous software requirements are key to the success of any software engineering project. Eliciting such requirements is a daunting task. In this paper, we present a framework that uses goal orientation as its main building blocks. Unlike other frameworks that have been reported in the literature, this framework strives to balance a compromise between formal methods on one hand and natural language on the other hand in specifying operations. A Chabot for covid-19 is presented to illustrate the framework.
Faster Depth Estimation for Situational Awareness on Urban Streets

Depth estimation algorithms are useful components of computer vision systems to assess video streams on urban streets. They can provide important information about the street space and improve situational awareness for humans using the street space. Deep learning algorithms for depth estimation algorithms are slow for these applications. To provide situational awareness to humans, the inference time needs to be small, so that results are fresh and meaningful. This paper explores two promising approaches - pruning and quantization - to improve inference time with a little compromise on the performance. We explore the impact of each of these methods independently and introduce a hybrid method that performs pruning followed by quantization. We evaluate the execution time, resource utilization, and performance of various state-of-the-art depth estimation algorithms with and without one or both of these techniques. We observe that using both pruning and quantization can improve the inference time dramatically, with a 39.6% speedup in inference time and an 81.2% reduction in memory utilization while losing only 4% in performance.
Efficient Text Normalization via Hybrid Bi-directional LSTM

Text normalization is the process of mapping non-sanctioned text to a standardized format in order to extract some meaningful inferences from it. Some important examples include computer interceded communication text like email, and instant messaging. Internet-based communication offers a generous amount of important crude data which require text normalization for further processing. Nonetheless, casual writing can immediately turn into a bottleneck for some natural language processing tasks. Off-the-rack tools are generally trained on proper content and cannot deal with the deformity in texts in an explicit manner. Current text normalization frameworks depend on phonetic closeness and arrangement models that work in a local fashion. Noticeably, handling contextual information is essential for this task. In this work, we propose a hybridized system of deep learning model that can viably fill in as a preprocessing step for NLP applications to adjust noisy content in texts. Empirical results demonstrate that the proposed encoder-decoder model based on bi-directional LSTM outperforms other text normalization mechanisms in terms of the model accuracy.
Development of Authentic Assessment Instruments Oriented Reading Skills Based Language Literacy through the Scaffolding Model

This research aims to develop an authentic assessment instrument product based on language literacy with a scaffolding model that is oriented towards reading skills. This research method uses research and development methods. The research results show that the preliminary research phase describes the results of curriculum analysis and student analysis. At the product development stage and validation by experts, the percentage of authenticity instrument eligibility consisting of three indicators is 82%. It can be concluded that the instrument is suitable for use in learning activities. The practicality stage given to 20 students in one of the Manyak Payed Public High Schools, Aceh Tamiang District, consisted of four alternative answers, 25% were declared practical. Thus, high school teachers are advised to use the scaffolding model as a solution to improve reading comprehension and language literacy skills.
Learning Deep Models: Critical Points and Local Openness

With the increasing popularity of nonconvex deep models, developing a unifying theory for studying the optimization problems that arise from training these models becomes very significant. Toward this end, we present in this paper a unifying landscape analysis framework that can be used when the training objective function is the composite of simple functions. Using the local openness property of the underlying training models, we provide simple sufficient conditions under which any local optimum of the resulting optimization problem is globally optimal. We first completely characterize the local openness of the symmetric and nonsymmetric matrix multiplication mapping. Then we use our characterization to (1) provide a simple proof for the classical result of Burer-Monteiro and extend it to noncontinuous loss functions; (2) show that every local optimum of two-layer linear networks is globally optimal. Unlike many existing results in the literature, our result requires no assumption on the target data matrix [Formula: see text], and input data matrix [Formula: see text]; (3) develop a complete characterization of the local/global optima equivalence of multilayer linear neural networks (we provide various counterexamples to show the necessity of each of our assumptions); and (4) show global/local optima equivalence of overparameterized nonlinear deep models having a certain pyramidal structure. In contrast to existing works, our result requires no assumption on the differentiability of the activation functions and can go beyond “full-rank” cases.
A Universal Audio Steganalysis Scheme Based on Multiscale Spectrograms and DeepResNet

Given the popularity of audio and video applications, compressed audio has become an important carrier of covert communication on the Internet. Many novel compressed audio steganography schemes have emerged that offer good hiding capability and aural concealment. In this paper, a universal steganalysis scheme called MultiSpecNet is proposed to detect steganography based on multiple embedding domains (advanced audio coding (AAC) and MPEG-1 Audio Layer III (MP3)), which are currently the two most popular compressed audio standards. The basic idea is that modification of either domain by a steganography scheme will change the time-frequency relationship of the audio signal after decoding. The proposed approach adopts the spectrogram as the input feature to extract richer information. DeepResNet is used to learn the distinguishing feature representations, and multiscale spectrograms are used to enrich the feature diversity. The experimental results show that the proposed scheme is effective at detecting different steganography schemes based on the AAC and MP3 embedding domains. The detection accuracy of the proposed scheme is higher than that achieved by other state-of-the-art schemes. Using spectrograms as the input, DeepResNet achieves better performance than schemes using quantized modified discrete cosine transform (MDCT) coefficients and mel-spectrogram, although the quantized MDCT coefficient is the parameter modified by the steganography schemes directly and mel-spectrogram is very popular and effective for general audio signal analysis. To the best of our knowledge, this work is the first audio steganalysis scheme that can detect multiple steganography schemes in both the MP3 and AAC embedding domains. The method proposed in this paper can be extended to audio steganalysis for other codecs or for audio forensics purposes.
How Profitable Is Bitcoin Mining?

Bitcoin is now as popular as any other currency. However, it is suspense, the Proof of Work. Is Bitcoin sustainable for the long term, and what are the factors that can protect it from being sustainable? Is it just the “Finance”? Or the complexity as well matters? Or we have a limit that will one day make it not usable anymore? Or it’s harmful to the environment? The top expert does say that we cannot have more than 21 million Bitcoin. They, however, quote as per current. However, they confirm that we can have more than 21 million Bitcoin. And hence there is no limit with Bitcoin. Fiat Currencies originated in 1971, and now we have realized that it has limitations, and the world is registering the loss each second considering the requirement for a centralized system in each body. Hence, we all want to leave the Fiat currency system now.