Reviewerly/exHarmony huggingface dataset:
Combined Image Enhancement for Recyclable Waste Object Detection In Low-Light Environment

An improved DETR target detection algorithm is proposed to address the problems of low accuracy and high false detection rate of detecting recyclable garbage by traditional target detection algorithms under low light environment. Firstly, the low illumination dataset is synthesized using gamma transform and Gaussian noise on the homemade recyclable garbage dataset to simulate the low illumination environment. Secondly, the ResNet50 network of the DETR model is replaced by the ResNeXt50 network, which improves the accuracy by a small margin, and the mAP0.5 is improved by 1.6%. Finally, to solve the problem of poor detection effect of the algorithm in low-light environment, the noise removal module and low-light enhancement module are added before the feature extraction network to significantly improve the detection performance of the DETR algorithm in low-light environment. The experimental results show that the average accuracy is improved by 25.5% to 96.4% compared with the conventional DETR model. The performance is also excellent compared with mainstream target detection models. When detecting recyclable garbage in a low-light environment, the algorithm can perform the detection task better and shows comparable detection results to those in normal environments.
Students' classroom Emotion Analysis Based on Intelligent Recognition

In order to further accurately obtain the classroom learning status of online students, a multimodal emotion recognition method was proposed to improve the accuracy of students' classroom emotion recognition. Convolutional neural network was used as speech recognition and facial expression recognition methods. The decision-level fusion method based on sum rule was used as multimodal information fusion method. The simulation results showed that the average recognition rate of the speech emotion recognition method proposed in this paper increases with the increase of training samples. When the learning rate is 0.001, the average recognition rate of the model is the highest, reaching 80.1%. The facial expression recognition method proposed in this paper is the most accurate for the recognition of approval and distracted expressions. Compared with other models, the multimodal emotion recognition method based on sum rule and decision-level fusion proposed in this paper has the highest recognition rate, reaching 87.99%. The above simulation results verified the superiority of the design of this paper and the necessity of improvement, and had certain practical reference value.
Fixed Point Theorems for Digital Images Using Path Length Metric

Till recent years, the Euclidean metric has been used to study the results pertaining to the digital images. Boxer [4] had suggested the use of path length metric for digital images as it is more appropriate compared to any other metric. In this paper, we define the path length metric in a different manner. Also, we derive the fixed point results for mappings satisfying quasi-contraction constraint using the same. As an application to the digital image processing, the path length metric is used to obtain the boundary of a given digital image and an algorithm for the same is demonstrated.
A Novel Efficient and Effective Preprocessing Algorithm for Text Classification

Text classification is an essential task of natural language processing.Preprocessing, which determines the representation of text features, is one of the key steps of text classification architecture.It proposed a novel efficient and effective preprocessing algorithm with three methods for text classification combining the Orthogonal Matching Pursuit algorithm to perform the classification.The main idea of the novel preprocessing strategy is that it combined stopword removal and/or regular filtering with tokenization and lowercase conversion, which can effectively reduce the feature dimension and improve the text feature matrix quality.Simulation tests on the 20 newsgroups dataset show that compared with the existing state-of-the-art method, the new method reduces the number of features by 19.85%, 34.35%, 26.25% and 38.67%, improves accuracy by 7.36%, 8.8%, 5.71% and 7.73%, and increases the speed of text classification by 17.38%, 25.64%, 23.76% and 33.38% on the four data, respectively.
Video Indexing for Live Nature Camera on Digital Earth

This paper presents a method of indexing video stored from live nature cameras on digital Earth and its application. Since two feature vectors from scene images are not necessary identical in the metrical space even if they are similar, it is hard to get benefit of indexing feature vectors. That is, the search system consequently has to fully access to almost whole feature vectors in the storage. The feature of the proposed system is that for indexing feature vectors, similarity-based indexing by clustering feature vectors is applied, where the feature vectore can be indexed with a centorid vector of the corresponding cluster. Consequently, the computing cost for accsessing to the objective images can be majorly reduced without the almost full access to whole feature vectors. For making our concept clear, we show an application scenario using video data from live streaming cameras that are opened to the public and some aerial or satellite images.
Cycle Detection and Clustering for Cyber Physical Systems

In this paper we present our work on cycle detection and clustering using unsupervised Machine Learning methods on manufacturing data. First we discuss the overall architecture of our cyber-physical system specially designed to gather large quantities of heterogeneous industrial data. Next, we detail several analysis steps, focusing on core tasks such as cycle detection and identification. Finally we show that even relatively simple data can be successfully used for predictive maintenance and fault detection.
Range Proofs with Constant Size and Trustless Setup

Range proofs are widely adopted in practice in many privacy-preserving cryptographic protocols in the public blockchain. The performances known in the literature for range proofs are logarithmic-sized proofs and linear verification time. In contexts where the proof verification is left to the ledger maintainers and proofs are stored in blocks, one might expect higher transaction fees and blockchain space when the size of the relation over the proof grows. With this paper, we improve Bulletproofs, a zero-knowledge argument of knowledge for range proofs, by modifying its Inner Product Argument (IPA) subroutine. In particular, we adopt a new relation from the polynomial commitment scheme of Halo, based on standard groups and assumptions (DLOG and RO) with a trustless setup. We design a two-step reduction algorithm and we obtain a constant number of two rounds in the IPA and a constant-sized proof composed of 5 $$\mathbb {G}_1$$ points and 2 $$\mathbb {Z}_p$$ scalars.
FROSTY: A High-Dimensional Scale-Free Bayesian Network Learning Method

We propose a scalable Bayesian network learning algorithm based on sparse Cholesky decomposition. Our approach only requires observational data and user-specified confidence level as inputs and can estimate networks with thousands of variables. The computational complexity of the proposed method is $O({p^{3}})$ for a graph with p vertices. Extensive numerical experiments illustrate the usefulness of our method with promising results. In simulation, the initial step in our approach also improves an alternative Bayesian network structure estimation method that uses an undirected graph as an input.
The extent of commercial banks’ readiness to implement blockchain technology

This study aimed to find out the readiness of Jordanian commercial banks to implement blockchain technology. The population of this study consisted of thirteen Jordanian banks. The study targeted the employees of the higher departments represented by the general managers and their deputies, and the employees of the middle departments represented by the directors and heads of the departments of each of the finance, internal auditing, and information technology departments in Jordanian banks. The study used the survey method to collect data and the SPSS Statistics 20.0 program to process and analyze the data. The most important results of the study were the presence of a statistically significant impact of the banks’ readiness with its dimensions (administrative readiness, professional readiness, organizational readiness, and legislative readiness) in the blockchain technology with its dimensions (the use of blockchain technology, hashing, information, and time imprint). As for the most important recommendations, it crystallizes the need for Jordanian banks to carry out a continuous updating process on the data within the block, and to apply periodic tests on the block operations to verify that there are no gaps in the chain.
Corporate Digital Identity Based on Blockchain

Digital Identity refers to the set of information that defines an entity, generally a person, which can be presented to other entities of digital world. This information represents different information such as skills, biological, activities and other data that may result useful. There are currently several projects developing digital identity systems for personal use and aiming to standardize its use among the population. Digital Identity finds in blockchain the support technology to enable this decentralized management in a secure and reliable way. Blockchain allows citizens identification to each application, contract or business, by providing only the information requested, and avoiding unnecessarily exposing of other private information about citizens. However, there are no digital identity systems for legal entities. Currently, the digital identity concept for business and corporations is limited to its appearance in the digital environment and their corporate image on internet. The objective of this work is the proposal and development of a digital identity for legal entities based on a blockchain network. The methodology is based on applying the same idea of personal digital identity. In addition, the proposal considers Ethereum blockchain since it is strongly accepted for the deployment of smart contracts. As a result, the proposed corporate digital identity provides companies the control of the information they share with government, people and other companies. In addition, the relationships between entities can be defined, and eventually automate, through smart contracts in the blockchain. The main limitation of this idea is the technology adoption by institutions and public administrations in order to normalizing digital relations between all agent involved: corporates, citizens and administrations.
The Role of Blockchain for Introducing Resilience in Insurance Domain: A Systematic Review

The insured market requires accuracy and agility to meet the requirements of customers, insurers, and other service providers. Some catastrophic events are impossible to avoid, and their negative effects impede the economic recovery of people and companies. Theft, illnesses, accidents, pandemics, and natural disasters are events that affect the possibility of ruin. In this context, the acquisition of an insurance contract allows the risk to be transferred to an insurance company and reduces uninsured losses. This market needs to maintain an audit trail of transactions, preserving the transparency of the information exchange process and the responsibility for the behavior of its participants. However, this segment faces new limitations related to market policies and risk coverage. Not all people can purchase insurance because they do not have banking services, which impedes the creation of economic resilience in populations that suffer from extreme poverty. Technologies such as blockchain offer a solution to financial exclusion and provide global financial access to anyone, without any discrimination. Blockchain technology provides reliability, transparency, and immutability to transactions using cryptographic techniques and distributed consensus methods, without the need for third parties to testify to the veracity of the events. With this consideration, the paper assesses how technology can address challenges, and identifies the future direction in terms of adoption within the insurance value chain. Finally, conclusions are provided for taking advantage of the versatility of blockchain in this business area.
Predicting Online Job Recruitment Fraudulent Using Machine Learning

Employing individuals via the Internet has been a boon for businesses in the modern day. It is much simpler and more convenient than traditional recruitment methods. However, several scammers are abusing this platform, which may result in financial and privacy loss for job seekers and damage to the reputable organisation's name. In this research, we proposed a technique for detecting Online Recruitment Fraud (ORF). This model uses a publicly available dataset containing 17,780 job postings. We apply the four classification models to determine which classification model performs best for our suggested model. In this model, we use decision trees, random forests, Naive Bayes and logistic regression methods. We have estimated and evaluated the accuracy of several prediction systems. The random forest classifier provides the greatest accuracy, 97.16%, on our dataset. We have endeavoured to develop a method for detecting bogus recruiting postings.
A Model for Artificial Conscience to Control Artificial Intelligence

We propose "Artificial Conscience - Control Module" framework to control the AI systems and to make them adaptable based on the user requirements. AI users can have different needs from the same AI system, and these systems must adjust their output based on these requirements. The proposed framework enables users to provide context to the AI system by assigning weights to different evaluation metrics. Based on these weights, AI metrics-agents negotiate with each other using our trust engine to output a solution with maximum "Artificial Feeling." This framework can be easily implemented in any AI system where multiple metrics are involved. We have illustrated the proposed framework using an AI system for classifying people based on income.
Uncertainty Handling with Type-2 Interval-Valued Fuzzy Logic in IoT Resource Classification

The growing supply of Internet-connected resources, often providing more than one service, add complexity to the procedures for discovering, classifying, and selecting the most appropriate resources to meet client demands. The specification of client preferences can lead to inaccuracies and uncertainties, as it depends on prior knowledge and experience for the correct details of parameters such as minimum, maximum, and measurement scales. This paper aims to address uncertainties in specifying and processing client preferences when classifying a set of discovered IoT (Internet of Things) resources. We propose a software architecture for resource discovery and classification in IoT called EXEHDA-Resource Ranking. The proposal stands out in IoT resource classification, exploring three approaches: (i) initial selection of resources with MCDA algorithm; (ii) pre-classification of newly discovered resources with machine learning; and (iii) treatment of uncertainty in preference processing using Type-2 Interval-valued Fuzzy Logic. In addition, one scenario containing resource request simulations applying different client preferences can be demonstrated in EXEHDA-RR features.
Zero-Knowledge Multi-transfer Based on Range Proofs and Homomorphic Encryption

Zero-knowledge proofs are widely adopted in Confidential Transactions (CTs). In particular, with these proofs, it is possible to prove the validity of transactions without revealing sensitive information. This has become an attractive property in public blockchain where transactions must be publicly verifiable. However, several challenges must be addressed in order not to alter important properties of the blockchain, such as not introducing trusted third parties and/or circuit-dependent trusted setups. Moreover, there are limited proposals working on the standard account model and considering extended payment models where multiple payees are involved in one transaction. With this paper, we first present our concept of Multi-Transfer (MT) in CTs settings, i.e., a transfer that involves multiple payees in a single transaction with privacy guarantees for balances and transfer amounts. Inspired by the work of Zether, we design the MT zero-knowledge proof system, named MTproof, by combining the aggregate version of Bulletproofs and several $$\Sigma $$ -Protocols to prove that an MT transaction is legit. We provide concrete evaluations of the MTproof in terms of proof size, prover and verifier execution time.
Dense Video Captioning Using Video-Audio Features and Topic Modeling Based on Caption

Videos are composed of multiple tasks. Dense video captioning entails captioning of different events in the video. A textual description is generated based on visual, speech and audio cues from a video and then topic modeling is performed on the generated caption. Uncertainty modeling technique is applied for finding temporal event proposals where timestamps for each event in the video are produced and also uses Transformer which inputs multi-modal features to identify captions effectively and to make it more precise. Topic modeling tasks include highlighted keywords in the captions generated and topic generation i.e., category under which the whole caption belongs to. The proposed model generates a textual description based on the dynamic and static visual features and audio cues from a video and then topic modeling is performed on the generated caption.
Accuracy Comparison of Neural Models for Spelling Correction in Handwriting OCR Data

In the present scenario, Handwriting Recognition (HWR) plays a very important role as due to current pandemic situations most of the exams are conducted in online mode. In HWR the device translates the user’s handwritten characters or words into readable by a computer system. The problem with HWR is there are different types of handwriting and different styles of writing each character which makes it difficult to analyze each letter uniquely and correctly also the major problem with HWR is when people write there is no specific font and font sizes are taken care which also plays important role in recognizing the letters. The paper presents an accuracy comparison system of spelling correction in Handwritten OCR data using four neural models BERT, SC-LSTM, CHAR-CNN-LSTM, CHAR-LSTM-LSTM. In task, sequence matcher algorithm is used for computation of similarity score between input text data and outputs of the different neural models at each iteration. It is observed that the BERT model gives the highest accuracy of 71.4% followed by SC-LSTM with 69.12%, CHAR-CNN-LSTM with 67.80% and CHAR-LSTM-LSTM with 69.34%.
Person re-identification accuracy improvement by training a CNN with the new large joint dataset and re-rank

The paper is aimed to improve person re-identification accuracy in distributed video surveillance systems based on constructing a large joint image dataset of people for training convolutional neural networks (CNN). For this aim, an analysis of existing datasets is provided. Then, a new large joint dataset for person re-identification task is constructed that includes the existing public datasets CUHK02, CUHK03, Market, Duke, MSMT17 and PolReID. Testing for re-identification is performed for such frequently cited CNNs as ResNet-50, DenseNet121 and PCB. Re-identification accuracy is evaluated by using the main metrics Rank, mAP and mINP. The use of the new large joint dataset makes it possible to improve Rank1 mAP, mINP on all test sets. Re-ranking is used to further increase the re-identification accuracy. Presented results confirm the effectiveness of the proposed approach.
A DWT-SVD Adaptive Digital Watermarking Algorithm Based on Chaotic Encryption

To address the problems of poor robustness and low security in traditional digital image watermarking, and the robustness and invisibility of watermarking cannot be combined, this paper proposes a DWT-SVD adaptive watermarking algorithm based on chaotic encryption. Firstly, the double encryption of the watermark is accomplished by Arnold dislocation and chaotic logistic sequence. Then, the invisibility of the watermark is achieved by using discrete wavelet transform, chunked discrete wavelet transform and singular value decomposition, and chunked characteristic adaptive embedding watermark. Finally, the extraction method of embedding watermark is designed. The experimental results show that the proposed method has good robustness and security under attacks such as shear attacks, compression attacks, and noise attacks. The algorithm has the features of high efficiency, high security, and strong resistance to attacks, which effectively solves the problems of poor robustness and low security of image watermarking and achieves the balance between robustness and invisibility.
Revisit Regularization Techniques for Gaze Estimation Generalization

Recent advances in gaze estimation are mainly dominated by deep learning like other computer vision tasks. While these deep methods get top results on gaze within-datasets evaluation, the model performance degrades dramatically when generalized to the target domain. The poor generalization performance prevents the deep methods from practical application. In this paper, we seek to improve the generalization performance of the deep neural network on gaze estimation. We found gaze estimation is suffered from the over-fitting problem, and propose to alleviate the problem with the technique of regularization: a) Weight Decay, b) Feature Normalization, and c) Weight Smoothing. Without extra computing resources or network parameters, our methods surpass all the gaze generalization benchmarks in experiments and reach the performance of the state-of-the-art unsupervised gaze domain generalization approaches.
CbwLoss: Constrained Bidirectional Weighted Loss for Self-Supervised Learning of Depth and Pose

Photometric differences are widely used as supervision signals to train neural networks for estimating depth and camera pose from unlabeled monocular videos. However, this approach is detrimental for model optimization because occlusions and moving objects in a scene violate the underlying static scenario assumption. In addition, pixels in textureless regions or less discriminative pixels hinder model training. To address these problems, in this paper, we deal with moving objects and occlusions by utilizing the differences between the flow fields, and the differences between the depth structure generated by affine transformation and view synthesis, respectively. Secondly, we mitigate the effect of textureless regions on model optimization by measuring the differences between features with more semantic and contextual information without requiring additional networks. In addition, although the bidirectionality component is used in each sub-objective function, a pair of images is reasoned about only once, which helps reduce overhead. Extensive experiments and visual analysis demonstrate the effectiveness of the proposed method, which outperforms existing state-of-the-art self-supervised methods under the same conditions and without introducing additional auxiliary information.