Reviewerly/exHarmony huggingface dataset:
Artist Based Video Recommendation System using Machine Learning

This study investigates the use of automatically derived visual characteristics in recommender systems and offers several novel contributions to the field of video recommendations. Because it is difficult for customers to see all of the videos in order to identify the ones they want, or even manually manage these large video collections. The Recommender System is one such engine, which suggests previously undiscovered material that the user may enjoy and assist them in making decisions. As a result, depending on the user's unique artist, id, alignment, identifying face edges, and extracting various characteristics from faces. The algorithm offers personalized sets of videos to users by recognizing human faces in low lighting condition, where the presence of noise in images, scales, masquerade in a diverse environment, and the discovery of unexplored artist videos remains a major obstacle that this work aims to resolve. In recent years, Deep Neural Network (DNN) has been demonstrated for a trained face detector which outperforms end-to-end long-standing approaches. The Support Vector Machine (SVM) is a key component in a range of applications, which includes image recognition, pattern recognition, and classification. One of the most promising areas of research in computer vision is face identification of different patterns. An approach to face recognition using deep learning techniques and an SVM model for face image identification is created in this work, which uses a DNN to extract features and SVM classification methods to identify the artist from videos.
Fundamentals of Blockchain and Distributed Ledger Technology (DLT)

Blockchain and distributed ledger technology have recently become essential for developing information networks. Although blockchain technology only stands for the recent methods of managing financial properties, its use is largely limitless. However, redistributing intelligence transparency into new democracy introduces new challenges and legalities, particularly in terms of security. This chapter explores the underlying principles of blockchain technologies and functional components about what constitutes a blockchain, and investigates a decentralized network’s vulnerabilities throughout the actual world. In the most profound stage, cryptographic algorithms are comprised of blockchain technologies. The taxonomy strengthens understanding of how blockchain has an effect on business models. It shows prospects for advances in the company model without making their complexity any simpler. We also have highlighted issues where specifications can theoretically solve the problems of varying degrees and foster creativity, development, and competition in the DLT/blockchain ecosystem. The propagation of the DL allows the network members to find consensus on the validity by a collection of guidelines for new data entries. A consensus process defined in the DL’s algorithmic architecture may be accomplished and can differ according to its structure, intent, and underlying characteristics. Innovative consensus protocols via a set of nodes make it easier to decentralize. In this chapter, these protocols are addressed such that perhaps the task of documenting payments and generating new blocks is subject to strict regulations that don’t favor any peers over any other. It is the cryptocurrency of Bitcoin that offers recognition and security for the technology, while the blockchains provide the encrypted, open, and decentralized exchange network of Bitcoin through five main blockchain enterprise concerns. There are many consensus mechanisms throughout the community of the blockchain environment, and even more go into the market. These algorithms are based on various targets, some of which we will explore in this section. Blockchain technology gives the consumers of existing systems a range of benefits. The first truly distributable and decentralized mechanism to maintain a common, trustworthy ledger is blockchain. This helps a network to record its existence and be sure that a malicious attacker cannot alter this history to his own advantage. Competing priorities, such as developing technologies, will guarantee authentication and privacy, and access to the public, and face significant engineering, legal, and operational challenges. It is the same if, upon request, laws mandate data to be deleted and, as the key function of the blockchain, contradict immutability. Blockchain technology, therefore, has multiple hardware authentication implementations. These critical tasks also require maximum reliability, privacy, and security.
Named Entity Recognition of Chinese Diabetic Literature by Integrating General Domain Knowledge

In the field of named entity recognition of diabetes literature, due to the lack of large-scale and high-quality annotation data sets, the traditional named entity recognition technology has the drawback of insufficient language feature extraction and poor generalization ability. In view of the above problems, this paper proposes an entity recognition method that integrates general domain knowledge for naming entity recognition in diabetes literature. This method uses Word2vec algorithm to generate feature level vectors based on Chinese diabetes literature. On this basis, the feature vectors are fused with the feature vectors generated by the pretraining model of BERT on the Chinese Wikipedia corpus, and uses principal component analysis to reduce the redundant feature vectors and enhance the expression ability of feature vectors in the general language scope. Then the context features are extracted by bidirectional long-term and short-term memory network, and finally the entity label sequence is generated by conditional random field decoding. The experiment proves that the model which integrates general domain knowledge is better than other traditional models in the recognition of Chinese diabetes literature, and the highest F1 value is 71.08%.
A Conversational Agent to Improve Response Quality in Course Evaluations

Recent advances in Natural Language Processing (NLP) bear the opportunity to design new forms of human-computer interaction with conversational interfaces. We hypothesize that these interfaces can interactively engage students to increase response quality of course evaluations in education compared to the common standard of web surveys. Past research indicates that web surveys come with disadvantages, such as poor response quality caused by inattention, survey fatigue or satisficing behavior. To test if conversational interfaces have a positive impact on the level of enjoyment and the response quality, we design an NLP-based conversational agent and deploy it in a field experiment with 127 students in our lecture and compare it with a web survey as a baseline. Our findings indicate that using conversational agents for evaluations are resulting in higher levels of response quality and level of enjoyment, and are therefore, a promising approach to increase the effectiveness of surveys in general.
Perspectives of face recognition with medical applications

Benefiting from its applications in engineering, face recognition already became hot topic. This paper will summarize the development trend of face recognition, analyze the medical application of face recognition and the application prospect of face recognition and deep learning in the field of medical engineering and discuss the possibility of applying face recognition in rehabilitation robot training.
Research on Computer Aided Learning System for the Hybrid Electric Vehicle Control Platform

The paper proposes the design and implementation of the teaching platform of the automotive engineering course learning system, and discusses the development ideas of the teaching resources of the automotive engineering learning system. The article explains the functional modules of the platform. Then it describes the network topology and system structure model of the teaching platform of the course learning system. Through the construction and practice of the experimental platform of the automotive engineering course learning system, the experimental teaching functions that are not available or difficult to complete in real experiments are realized, and the engineering practice ability and scientific research innovation ability of students are improved.
One Simple Method of Positioning and Segmenting Dot Matrix Characters for Recognition

Dot matrix characters are printed on the packaging boxes or plastic bags of food or other products by inkjet printers, recording the manufacturing date and product ID. This information is of much significance for product trace and quality control. As dot matrix characters are prone to printing errors due to printer vibration, it is essential to check whether the printed characters can be recognized or not after printing. Most OCR (optical character recognition) technologies only recognize continuous characters and very few are devised for dot matrix characters, i.e., discontinuous ones. The major difficulties of dot matrix characters recognition lie in segmentation and positioning due to their discontinuous dot structures. In the paper, one effective and efficient recognition package has been developed and one simple but valid method is proposed to accurately position and segment dot matrix characters and it can handle the characters of adhesions and discontinuities. Two popular segmentation methods are selected as benchmarks and the experimental results show that the proposed method has the best effects of segmentation and recognition.
Software Specification

chapter Share on Software Specification Authors: Ian J. Hayes Search about this author , Steve King Search about this author Authors Info & Claims Theories of Programming: The Life and Works of Tony HoareOctober 2021 Pages 251–270https://doi.org/10.1145/3477355.3477367Online:02 October 2021Publication History 0citation2DownloadsMetricsTotal Citations0Total Downloads2Last 12 Months2Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Mining of High-Utility Patterns in Big IoT Databases

In general data mining, HUIM also known as high-utility itemset mining is an offshoot of frequent item set mining (FIM). HUIM is known to give more emphasis to many factors which can give HUIM a distinct edge over FIM. PHIUM, or Potential high-utility item set mining has been created to give intrinsic patterns in databases that tend to be uncertain. Despite most previous methods being highly effective and powerful miners, PHUIM needs to work fast. Most current mining techniques do not handle databases with extremely large number of records when performing HUIM. In this paper, we make the assumption that the dataset is bigger than a direct load into RAM could handle. Furthermore, the dataset is not of the size where modification or duplication is possible, and as such a MapReduce framework is created that can be used to handle datasets that fall into these categories. One of the main objectives of this research is to be able to reduce the frequency of database scans while simultaneously maximizing parallel processing. Using experimental analysis, our Hadoop based algorithm performs well to mine high utility itemsets from big databases.
Evolving Architectures With Gradient Misalignment Toward Low Adversarial Transferability

Deep neural network image classifiers are known to be susceptible, not only to adversarial examples created for them, but also to those created for others. This phenomenon poses a potential security risk in various black-box systems that rely on image classifiers. One of the observations on networks that have transferability of adversarial examples between them is the similarity of their architectures. Networks with high architectural similarity tend to share high transferability as well. Thus, in this study, we address this problem from a novel perspective by investigating the contribution of network architecture to transferability. Specifically, we propose an architecture searching framework that employs neuroevolution to evolve network architectures and gradient misalignment loss to encourage networks to converge into dissimilar functions after training. Our findings indicate that the proposed framework successfully discovers architectures that reduce transferability from four standard networks, including ResNet and VGG, while maintaining good accuracy on unperturbed images. In addition, the evolved networks trained with gradient misalignment exhibit significantly lower transferability than a standard network trained with gradient misalignment, which indicates that network architecture plays an important role in reducing transferability. We demonstrate that designing or exploring proper network architectures is a promising approach to tackle the transferability issue and train adversarially robust image classifiers.
Anti-Money Laundering Recognition through the Gradient Boosting Classifier

Perhaps the most disturbing danger to the solidness and progress of the economy of the world is the beast called illegal tax avoidance. To forestall this beast and the frightful harms it causes, certain rules are set up. These rules are called Against Cash rules. For a smooth methodology concerning tax evasion, it has been suggested that monetary establishments move away from moving toward those that are rule-arranged and hazardous, to those that current danger as their principal drivers. This research aims to explore the exhibition of a slope boosting calculation in distinguishing tax evasion exercises. Two structures were utilized to accomplish the goal of this investigation. The selected structures comprise, the module works out/test division examination that is for disconnected learning just as a prequential investigation that is for internet learning. In the workout/test partition investigation, disconnected models are developed genuinely as they are worked out on a fixed information partition to work out and test gatherings. Then again, the prequential examination agreed to dissect online students by reproducing an unbounded information stream, and it prepares likewise. The accuracy, sensitivity, recollect, F1-score, non-linear data, and other metrics were used to assess the described classifiers. F1-score over time steps, box plots (when temporal information is available). Because of the nature of the classifiers, the assessment proceeded through hundred repetitions for the reason that it was non-deterministic. The Light gradient Boosting Algorithm (LGBA) and XGBoost outflanked the Random Forest algorithm in distinguishing illegal exercises both at an exchange and account level, as appeared in Table 2. Measurable importance was identified when applying the Wilcoxon signed-rank test (α = 0.05) in terms of review, exactness, and F1-Score. The CatBoost algorithm did not result on standard with the other gradient boosting prototypes, and so, was dropped in ensuing tests. The utilization of both neighborhood and amassed highlights on the Euclidean data conveyed better outcomes.
Cross-View Images Matching and Registration Technology Based on Deep Learning

Cross view images matching and registration is to extract the images features from different views of the same scene, and measure the similarity between features by measuring the correspondence between images, then perform pixel-level registration. Cross-view images have problems such as poor stability of feature points and big difference in scale, resulting in low efficiency of matching and registration using traditional methods. In this paper, a novel image matching method based on deep learning is adopted. Firstly, a convolutional neural network is utilized for image matching to achieve the initial selection of the target, and then the pixel level registration is carried out for the successfully matched images. Considering the problem of small samples, four feature extraction networks are used for feature extraction to achieve knowledge transfer. In the aspect of image registration, considering the difference between feature descriptors and baselines of cross view images, deep learning is introduced to improve the traditional algorithm to achieve accurate registration.
Maliciously-Secure MrNISC in the Plain Model

We study strong versions of round-optimal MPC. A recent work of Benhamouda and Lin (TCC ’20) identified a version of secure multiparty computation (MPC), termed Multiparty reusable Non-Interactive Secure Computation (MrNISC), that combines at the same time several fundamental aspects of secure computation with standard simulation security into one primitive: round-optimality, succinctness, concurrency, and adaptivity. In more detail, MrNISC is essentially a two-round MPC protocol where the first round of messages serves as a reusable commitment to the private inputs of participating parties. Using these commitments, any subset of parties can later compute any function of their choice on their respective inputs by broadcasting one message each. Anyone who sees these parties’ commitments and evaluation messages (even an outside observer) can learn the function output and nothing else. Importantly, the input commitments can be computed without knowing anything about other participating parties (neither their identities nor their number) and they are reusable across any number of computations. By now, there are several known MrNISC protocols from either (bilinear) group-based assumptions or from LWE. They all satisfy semi-malicious security (in the plain model) and require trusted setup assumptions in order to get malicious security. We are interested in maliciously secure MrNISC protocols in the plain model, without trusted setup. Since the standard notion of polynomial simulation is un-achievable in less than four rounds, we focus on security with super-polynomial-time simulation (SPS). Our main result is the first maliciously secure SPS MrNISC in the plain model. The result is obtained by generically compiling any semi-malicious MrNISC and the security of our compiler relies on several well-studied assumptions of an indistinguishability obfuscator, DDH over $${\mathbb Z} ^*_{p}$$ and asymmetric pairing groups, and a time-lock puzzle (all of which need to be sub-exponentially hard). As a special case, we obtain the first 2-round maliciously secure SPS MPC based on well-founded assumptions. This MPC is also concurrently self-composable and its first message is short (i.e., its size is independent of the number of the participating parties) and reusable throughout any number of computations. Prior to our work, for two round maliciously secure MPC, neither concurrent MPC nor reusable MPC nor MPC with first message independent in the number of parties was known from any set of assumptions. Of independent interest is one of our building blocks: the first construction of a one-round non-malleable commitment scheme from well-studied assumptions, avoiding keyless hash functions and non-standard hardness amplification assumptions. The full version of this paper can be found at [26].
An Improved Pheromone-Based Kohonen Self- Organising Map in Clustering and Visualising Balanced and Imbalanced Datasets

The data distribution issue remains an unsolved clustering problem in data mining, especially in dealing with imbalanced datasets. The Kohonen Self-Organising Map (KSOM) is one of the well-known clustering algorithms that can solve various problems without a pre- defined number of clusters. However, similar to other clustering algorithms, this algorithm requires sufficient data for its unsupervised learning process. The inadequate amount of class label data in a dataset significantly affects the clustering learning process, leading to inefficient and unreliable results. Numerous research have been conducted by hybridising and optimising the KSOM algorithm with various optimisation techniques. Unfortunately, the problems are still unsolved, especially separation boundary and overlapping clusters. Therefore, this research proposed an improved pheromonebased PKSOM algorithm known as iPKSOM to solve the mentioned problem. Six different datasets, i.e. Iris, Seed, Glass, Titanic, WDBC, and Tropical Wood datasets were chosen to investigate the effectiveness of the iPKSOM algorithm. All datasets were observed and compared with the original KSOM results. This modification significantly impacted the clustering process by improving and refining the scatteredness of clustering data and reducing overlapping clusters. Therefore, this proposed algorithm can be implemented in clustering other complex datasets, such as high dimensional and streaming data.
Numerical Exploration of Training Loss Level-Sets in Deep Neural Networks

We present a computational method for empirically characterizing the training loss level-sets of deep neural networks. Our method numerically constructs a path in parameter space that is constrained to a set with a fixed near-zero training loss. By measuring regularization functions and test loss at different points within this path, we examine how different points in the parameter space with the same fixed training loss compare in terms of generalization ability. We also compare this method for finding regularized points with the more typical method, that uses objective functions which are weighted sums of training loss and regularization terms. We apply dimensionality reduction to the traversed paths in order to visualize the loss level sets in a well-regularized region of parameter space. Our results provide new information about the loss landscape of deep neural networks, as well as a new strategy for reducing test loss.
Analysis and Identification of Pipeline's Work Efficiency Based on OpenPose

Currently, the majority of the industrial assembly lines of domestic and even global enterprises still need manual operation. Especially in some production process which is more complex, manual operation is essential. In such a large-scale manual operation, the efficiency of it is very important. Compared with the method of judging the working efficiency of workers by stopwatch, it is more intuitive and analyzable to analyze the motion curve of workers through the video collected by the camera on the industrial assembly line. This paper uses the video data collected by industrial cameras and constructs a computer vision system that can automatically judge the work efficiency of workers by human posture recognition algorithm based on OpenPose.
Estimation of heat generation by using echo state network for diagnosing thermal characteristics

Temperature measurement can be an effective method for diagnosing anomalies in equipment. The amount of internally generated heat may be due to load or an anomaly. Therefore, it is desirable to know how much heat was generated at what time. Echo State Network (ESN), which expresses physical heat transfer in a form of neural network, is a promising method for estimating the amount of heat by performing inverse processing of temperature changes. The authors have developed a method to learn the inverse transfer characteristics by using ESN. This paper reports the detail of estimation of heat generation along with the results using synthesized and measured data.