Reviewerly/exHarmony huggingface dataset:
Inverse Representation Inspired Multi-Resolution Dictionary Learning Method for Face Recognition

Face recognition is widely used and is one of the most challenging tasks in computer vision. In recent years, many face recognition methods based on dictionary learning have been proposed. However, most methods only focus on the resolution of the original image, and the change of resolution may affect the recognition results when dealing with practical problems. Aiming at the above problems, a method of multi-resolution dictionary learning combined with sample reverse representation is proposed and applied to face recognition. First, the dictionaries associated with multiple resolution images are learnt to obtain the first representation error. Then different auxiliary samples are generated for each test sample, and a dictionary consisted of test sample, auxiliary samples, and other classes of training samples is established to sequentially represent all training samples at this resolution, and to obtain the second representation error. Finally, a weighted fusion scheme is used to obtain the ultimate classification result. Experimental results on four widely used face datasets show that the proposed method achieves better performance and is effective for resolution change.
Gesture Controlled Assistive Device for Deaf, Dumb and Blind People Using Raspberry-Pi

Focusing and Addressing the problems of Visually(Who Can't see),Audibly(who can't hear),Vocally(who can't talk),through a single device is a tough task. After researching on this a convenient assistive device can be made for them using latest devices like Raspberrypi, Windows, MAC etc. The main purpose of the device is to make the differently abled people, feel independent confident by seeing, hearing and talking for them. The device provides Raspberry Pi/Windows based aid for the blind,deaf and dumb people. The proposed device uses CNN (Convolutional neural networks) algorithm. It is a special type of neural network used for image classification. In this project CNN lies convolution, an operation highly specialized at detecting patterns in images like blurring, sharpening, and outlining to images is done depending on the collected dataset. The indications of hands are simply discovered and shown with the help of this algorithm. The device does Image to text conversion and it is displayed on the Monitor screen of the device in this case I have used monitor instead of LCD screen. The user (Deaf and Dumb people) can understand and what next to communicate with them.The model has 2 options to select the required conversion technique.•Image to Gesture (ITT)•Gesture to Text(GTT)
Adaptive anomaly detection-based liquid loading prediction in shale gas wells

Liquid loading is one of the main factors that can severely impede shale gas production. It is crucial for efficient operation of gas production to forecast the liquid loading in real time. Most of the existing techniques for liquid loading prediction are based on physical modeling, which are generally limited by human knowledge of the underlying mechanism of liquid loading formation. In this study, we propose a data-driven method for liquid loading prediction in shale gas wells based on deep anomaly detection technology. In particular, we employ deep neural networks to learn the normal behavior of a well and test a given sequence for anomaly by measuring the sequence reconstruction errors of the neural network with adaptive threshold. Unlike existing physical model-based methods, our method makes no assumptions about the cause of liquid loading, while the prediction results are merely determined by historical records. To verify the effectiveness of our method, we conduct experiments on real shale gas production data coming from 73 shale gas wells and the precision of method is 85.71%. The experimental results demonstrate that our method surpasses existing physical modeling based methods at prediction accuracy and timeliness.
Information: 'I' vs. 'we' vs. 'they'

Seeking a balance between protecting and using personal data.
STCDesc: Learning deep local descriptor using similar triangle constraint

Triplet loss is widely used to detect learned descriptors and achieves promising performance. However, triplet loss fails to fully consider the influence of adjacent descriptors from the same type of sample, which is one of the main reasons for image mismatching. To solve this problem, we propose a descriptor network based on triplet loss with a similar triangle constraint, named as STCDesc. This network not only considers the correlation between descriptors from different types of samples but also considers the relevance of descriptors from the same type of sample. Furthermore, we propose a normalized exponential algorithm to reduce the impact of negative samples and improve calculation speed. The proposed method can effectively improve the stability of learned descriptors using the proposed triangle constraint and normalized exponential algorithm. To verify the effectiveness of the proposed descriptor network, extensive experiments were conducted using four benchmarks. The experimental results demonstrate that the proposed descriptor network achieves favorable performance compared to state-of-the-art methods.
CLAP-PRE: Certificateless Autonomous Path Proxy Re-Encryption for Data Sharing in the Cloud

In e-health systems, patients encrypt their personal health data for privacy purposes and upload them to the cloud. There exists a need for sharing patient health data with doctors for healing purposes in one’s own preferred order. To achieve this fine-gained access control to delegation paths, some researchers have designed a new proxy re-encryption (PRE) scheme called autonomous path proxy re-encryption (AP-PRE), where the delegator can control the whole delegation path in a multi-hop delegation process. In this paper, we introduce a certificateless autonomous path proxy re-encryption (CLAP-PRE) using multilinear maps, which holds both the properties (i.e., certificateless, autonomous path) of certificateless encryption and autonomous path proxy re-encryption. In the proposed scheme, (a) each user has two public keys (user’s identity and traditional public key) with corresponding private keys, and (b) each ciphertext is first re-encrypted from a public key encryption (PKE) scheme to an identity-based encryption (IBE) scheme and then transformed in the IBE scheme. Our scheme is an IND-CPA secure CLAP-PRE scheme under the k-multilinear decisional Diffie–Hellman (k-MDDH) assumption in the random oracle model.
Facial Emotion Classification of Multi-Type Datasets based on SVM Classifier

This paper presents an approach to using 2D images of stylized characters with annotated facial expressions and human facial expressions from the Facial Expression Research Group 2D Database (FERG-DB) and Extended Cohn-Kanade Database (CK+) consecutively. This is to improve the quality of Facial Expression Recognition (FER) for classifying facial expressions of human faces and cartoon animated faces, which will benefit the improvement of acting towards the correct emotion. A geometric and appearance hybrid approach to feature extraction was used to gain essential components. Principal component analysis (PCA) was used to reduce the dimensionality of the features. Support Vector Machine (SVM) classified selected features into six emotions: fear, anger, joy, sadness, surprise, and contempt. The system achieves a recognition rate of 92.63&#x0025;.
A Novel Semantic-Enhanced Text Graph Representation Learning Approach through Transformer Paradigm

Among common tasks in natural language processing (NLP) domain, text classification is considered as an important primitive task which is widely applied in multiple disciplines. Recent advanced deep learning-based architectures such as sequence-to-sequence (seq2seq) with attention mechanism have demonstrated remarkable improvements in multiple NLP’s tasks, including classification. However, recent seq2seq-based models still encounter challenges related to the limitation in effectively capturing long-range dependent relationships between words in a text corpus. Recent integrated graph neural network and textual graph transformer (TGT)-based models have demonstrated significant improvements in preserving the structural n-hop co-occurring relationships between words in a given text corpus. However, these models still suffer problems related to the thorough considerations on the sequential and contextual relations of words within a single document’s graph. To meet these challenges, in this article we proposed a novel semantic-enhanced graph transformer-based textual representation learning approach, called as: SemTGT. Our proposed SemTGT can support to effectively learn both local rich-contextual and global long-range structural latent representations of texts for leveraging the performance of classification task. Extensive experiments in standard datasets demonstrate the effectiveness of our proposed SemTGT model in comparing with recent seq2seq-based and textual graph embedding-based baselines.
Intention Recognition With ProbLog

In many scenarios where robots or autonomous systems may be deployed, the capacity to infer and reason about the intentions of other agents can improve the performance or utility of the system. For example, a smart home or assisted living facility is better able to select assistive services to deploy if it understands the goals of the occupants in advance. In this article, we present a framework for reasoning about intentions using probabilistic logic programming. We employ ProbLog, a probabilistic extension to Prolog, to infer the most probable intention given observations of the actions of the agent and sensor readings of important aspects of the environment. We evaluated our model on a domain modeling a smart home. The model achieved 0.75 accuracy at full observability. The model was robust to reduced observability.
Extracting and Archiving Data from Social Media to Support Cultural Heritage Preservation in Nineveh

During the last decades, various aspects of Nineveh&#x0027;s cultural heritage have been destroyed during wars or natural causes. Therefore, the needs to preserve these valuable heritages become crucial. With the increased use of the Internet, Social media networks have become part of peoples&#x0027; daily lives for publicly sharing information, including their feelings, opinion expression, knowledge, and sharing images, videos, audio, and even their locations. This paper aims to gather Nineveh&#x0027;s cultural heritage data from different social media sites. We prepare it to be used for supporting the preservation of the cultural heritage process, including both tangible and intangible heritage. With social media data, python programming language, and web scraping, various data types can be fetched from different heterogeneous sources such as Twitter, YouTube, etc., depending on several keywords and hashtags. Once the data is collected, several pre-processing operations are implemented to clean, organize and archive the resulted data in the NoSQL database and Amazon Simple Storage Service (Amazon S3). The archived cleaned information can be used later to query, browse, analyze and visualize the target information.
Improved Fragile Watermarking Technique Using Modified LBP Operator

A method of information concealment known as digital watermarking protects multimedia material against unauthorized copying and dissemination by embedding the essential information in the original data. This paper presents an improved fragile watermarking method using a modified Local Binary Pattern (LBP) operator by using local pixel contrast for embedding the watermark. An improved LBP operator has been applied to the cover image to indicate rough and smooth blocks. Then the least significant bit embedding technique is used to embed watermarking bits in the cover image, in which 2&#x2013;3 bits are embedded in the rough block and 1&#x2013;2 bits in the smooth block, respectively. The efficiency and performance of this proposed technique are evaluated based on PSNR, MES, and payload. This method uses a watermark size of 128&#x002A;128 to be embedded in a cover image of size 512&#x002A;512. It gives a PSNR of 54.420 and an MSE of 0.234. The proposed technique achieves good invisibility, ensures better imperceptibility, and shows better robustness.
ESC: Extractive Summary Construction for Ranking of Customer Criticism using Artificial Neural Networks

In the world we are living today, just knowing the methods of performing a certain task is not important. The procedure is equally necessary to be known and implemented. Conducting research and finding out the best and most efficient algorithm is needed to begin with, but after that implementing that algorithm is also a major part of the research. Summarization and visualization alike, have many different algorithms and libraries that are included for implementation. For summarization, there are certain important steps that are followed. Since, this paper is based on aspect-based summarization, so aspect tagging along with the sentiment classification is done using neural networks techniques. Visualization is a simple but confusing process. It consists of various libraries that are used according to the requirement of the output. The complications in visualizing arise when the dimensions and the basis of pictorial representation have to be identified. In this work, we have used Recurrent Neural Network technique for aspect tagging and Convolutional Neural Network for sentiment classification. This work mainly focuses on summarization of customer reviews of restaurants based on aspects.
A White-Box SM4 Implementation by Introducing Pseudo States Applied to Edge IoT Agents

With the widespread application of power Internet of Things (IoT), the edge IoT agents are often threatened by various attacks, among which the white-box attack is the most serious. The white-box implementation of the cryptography algorithm can hide key information even in the white-box attack context by means of obfuscation. However, under the specially designed attack, there is still a risk of the information being recovered within a certain time complexity. In this paper, by introducing pseudo states, a new white-box implementation of SM4 algorithm is proposed. The encryption and decryption processes are implemented in the form of matrices and lookup tables, which are obfuscated by scrambling encodings. The introduction of pseudo states could complicate the obfuscation, leading to the great improvement in the security. The number of pseudo states can be changed according to the requirements of security. Through several quantitative indicators, including diversity, ambiguity, the time complexity required to extract the key and the value space of the key and external encodings, it is proved that the security of the proposed implementation could been enhanced significantly, compared with the existing schemes under similar memory occupation.
Tracing semantic change in Portuguese

Abstract This study uses word embeddings to investigate the semantic changes underlying the creation of two adversative connectives in Portuguese, porém and mas ‘but, however’. For porém , we chart its development from an original PP formed by a preposition with a causal meaning ( por ) and a demonstrative pronoun that referred anaphorically to a previous proposition ( en ( de )). For mas , we trace its change from an adverb meaning ‘more’. Adopting a distributional semantics approach, we use word embedding models trained on two corpora, the CIPM ( Corpus Informatizado do Português Medieval , containing texts from the 12th–16th centuries) and COLONIA (containing texts from the 16th–20th centuries). We produce a measure of change based on the similarity scores of porém and mas with respect to words in relevant semantic categories in each corpus, representing the source and the target meanings. This paper, which constitutes the first computational study of semantic change in Portuguese, also discusses challenges and outlines steps to be taken into consideration when choosing embedding algorithms for small historical corpora.
Functional dimension reduction based on fuzzy partition and transformation

Summary Functional sliced inverse regression (FSIR) is the among most popular methods for the functional dimension reduction. However, FSIR has two evident shortcomings. On the one hand, the number of samples in each slice must not be too small and selecting a suitable S is difficult, particularly for data with small sample size, where S indicates the number of slices. On the other hand, FSIR and its related methods are well‐known for their poor performance when the link function is an even (or symmetric) dependency. To solve these two problems, we propose three new types of estimation methods. First, we propose the functional fuzzy inverse regression (FFIR) method based on a fuzzy partition. Compared with FSIR that uses a hard partition, the fuzzy partition uses all samples with different weights to estimate the mean in each slice. Therefore, FFIR exhibits good performance even for data with small sample size. Second, we suggest two transformation approaches, namely, FSIRR and FSIRP, avoiding the symmetric dependency between the response and the predictor. FSIRR eliminates the symmetric dependency by transforming the response variable, while FSIRP overcomes the symmetric dependency by transforming the functional predictor. Third, we propose the FFIRR and FFIRP methods by combining the advantages of FFIR and two transformation methods. FFIRR and FFIRP replace the FSIR method on the transformation data via FFIR. Simulation and real data analysis show that three types of proposed methods exhibit better performance than FSIR in terms of the estimation accuracy and stability.
Multi-View Information Bottleneck Without Variational Approximation

By "intelligently" fuse the complementary information across different views, multi-view learning is able to improve the performance of classification task. In this work, we extend the information bottleneck principle to supervised multi-view learning scenario and use the recently proposed matrix-based Rényi’s α-order entropy functional to optimize the resulting objective directly, without the necessity of variational approximation or adversarial training. Empirical results in both synthetic and real-world datasets suggest that our method enjoys improved robustness to noise and redundant information in each view, especially given limited training samples. Code is available at https://github.com/archy666/MEIB.
Clustering by hybrid K-Means and black hole entropic fuzzy clustering algorithm for medical data

Today clustering-based machine learning algorithms are the important field in data mining. Here, medical data clustering is one of the core applications of data mining to predict and identify the risk factor of the disease. At the same time, medical data clustering is a very important and challenging task due to its complexity and high frequency of data. In order to achieve proper data clustering, this paper proposed a hybrid data clustering algorithm by the combination of [Formula: see text]-Means and Black Hole Entropic Fuzzy Clustering (BHEFC). [Formula: see text]-Means is the first and one of the most popular and low-computation cost partitioned-based clustering algorithms. There are two modules in this hybrid clustering, first some number of iterations are executed by the first module of this hybrid clustering algorithm, which is [Formula: see text]-Means clustering. After some number of iterations, the clustering solutions are shifted to the second module of this hybrid clustering algorithm, which is Entrophic Fuzzy Clustering. So, it can get the advantages of both algorithms. [Formula: see text]-Means clustering algorithm can produce fast clustering solution due to its low-computation cost. But it can go for premature convergence. To overcome this problem, the second module used BHEFC, which can use large amount of high-frequency medical data. The experimental results are done with the medical practitioners to predict the risk factors of the heart disease patients and doctors can give the suggestions based on the risk factors. Finally, the efficiency of the proposed Hybrid [Formula: see text]-Means and BHEFC is analyzed by three different performance measures.
Analysis of the Role of HS-HKRVM Analytic Hierarchy Process in the Evaluation of English Teaching Quality

With the development of global market integration, the demand for English professionals in various industries is growing, and the professional standards for English professionals are also getting higher and higher. Therefore, English education is also faced with many opportunities and challenges; especially, the comprehensive education quality of English majors is becoming more and more prominent. The traditional teaching method using test scores as the evaluation standard leads to boring courses, students are not very interested in learning, and they cannot appreciate the charm of disciplines and majors, resulting in prominent problems in teaching form. This paper first analyzes the idea of the English learning quality evaluation system and then analyzes the English learning quality evaluation system based on HS-HKRVM. This paper also analyzes the construction of the English learning quality evaluation system based on the analysis layer process and finally studies the construction of the English learning quality evaluation system based on HS-HKRVM. Experiments show that HS-HKRVM can play a great role in the evaluation and improvement of English teaching quality.
NeutroAlgebra and NeutroGeometry for Dealing the Heteroclinic Patterns

Recently, dealing the heteroclinic data and its pattern finding has been considered as one of major concern for the researchers. It becomes more complex in the case of dynamic or periodic uncertainty in data. These types of data form heteroclinic orbit, which changes the space from equilibrium to non-equilibrium and vice versa. In this case, several parallel lines can be drawn from the given point to measure the indeterminacy, which is used to be find flight paths. It means these types of data do not follow the fifth law. To deal with them, following the NeutroAlgebra, a new geometry called as NeutroGeometry is studied in this chapter to characterize them based on Euclidean, non-Euclidean, and indeterminate zones.
Application of artificial intelligence APP in quality evaluation of primary school science education

Two scientific and systematic evaluation index systems are constructed for primary education APP and Science, Technology, Engineering, and Mathematics (STEM) courses. These systems are revised and confirmed through two rounds of Delphi method. The Delphi method determines an educational application (APP) evaluation index system containing four first-level indicators and thirty-three second-level indicators and a primary school STEM curriculum evaluation index system containing five first-level indicators and nineteen second-level indicators. Finally, analytic hierarchy process is used to determine the weight of all indicators to rank the weights. In the evaluation index system of educational APP, the weight of teaching design is 0. 2900, ranking the first; the weight of interactive experience is 0.1801, ranking the last. In the STEM curriculum evaluation index system, the weight of innovation practice is 0.3472, which is the maximum; the weight of independent management is 0.0879, which is the minimum. It is of great significance to promote the deepening of primary school STEM curriculum learning evaluation, improve the effectiveness of STEM curriculum teaching, and promote the healthy development of primary school education APP.