Reviewerly/exHarmony huggingface dataset:
Rows from Many Sources: Enriching row completions from Wikidata with a pre-trained Language Model

Row completion is the task of augmenting a given table of text and numbers with additional, relevant rows. The task divides into two steps: subject suggestion, the task of populating the main column; and gap filling, the task of populating the remaining columns. We present state-of-the-art results for subject suggestion and gap filling measured on a standard benchmark (WikiTables).
An Informatization Model of Scientific Computing for Mining Association Rules Used in Teaching Management Evaluation

The management information system is a kind of information-based, people-oriented, and socialized service as its purpose. It adopts advanced technology to realize the management of teachers, students, and other related personnel while effectively improving the quality of teaching. This article first analyzes the overview of data mining technology. Secondly, it analyzes the related algorithms of association rules. Finally, this article introduces the demand analysis of the university teaching quality evaluation system based on the scientific calculation of association rules. According to the demand analysis results, the system module and the corresponding submodule function modules are determined, and finally, the system architecture model is designed. Experimental results show that this model is useful for providing the effectiveness of information management.
The training response law explains how deep neural networks learn

Abstract Deep neural network is the widely applied technology in this decade. In spite of the fruitful applications, the mechanism behind that is still to be elucidated. We study the learning process with a very simple supervised learning encoding problem. As a result, we found a simple law, in the training response, which describes neural tangent kernel. The response consists of a power law like decay multiplied by a simple response kernel. We can construct a simple mean-field dynamical model with the law, which explains how the network learns. In the learning, the input space is split into sub-spaces along competition between the kernels. With the iterated splits and the aging, the network gets more complexity, but finally loses its plasticity.
Research on English Hybrid Assisted Teaching System Using Contextual Support of R-CNN

The developing countries and developed countries are depending on major as well as single language, i.e., English. The software tool level English teaching assistance models are available but those are not that user-friendly and also unable to support current technology. The earlier English teaching assistance techniques like RFO (Random Forest Optimization) machine learning, Xboosting machine learning, and SVM (Support Vector Machine) cannot support background provision. English teachers must find a way to coordinate the relationships between students, teachers, the learning environment, and learning strategies. A modern user-friendly building technique and an advanced ecobalancing of education are suggested and executed, which may increase the hybrid education characteristics and request skills of English. Therefore, an advanced ANN-(artificial neural network-) based hybrid English teaching assistance model is inevitability necessary. In this research work, R-CNN-based ANN model is imported to make applications for hybrid English teaching assistants. The performance measures like accuracy 97.89%, sensitivity 98.34%, recall 94.83%, and throughput 92.89% had attained. The implemented design is competing with present technology and outstrips the methodology. The process is outstripped by the realized design, which competes with current technology.
Parallel CNN Network Learning-Based Video Object Recognition for UAV Ground Detection

Video object recognition for UAV ground detection is widely used in target search, daily patrol, environmental reconnaissance, and other fields. So, we propose the novel parallel deep learning network with the ability of the global and local joint feature extraction for the UAV video target detection. This paper focuses on solving the problems of feature extraction and target background discrimination required by target discovery to realize target discovery. Break through the key problems of real-time target recognition, such as multiscale targets, high background complexity, many small targets, dense target arrangement, and multidirection, and put forward an optimized network scheme, aiming at the problem of multiscale of image target and aiming at the problem of large change of target scale in image. In the network, the corresponding targets with different sizes and different aspect ratios are matched to make the different targets match the closest, and then, the position of the detection box is fine-tuned by regression. For the special problem of image viewing angle and for the rotation invariance of the airborne down looking image of the target, the usual solution is through data enhancement; that is, through the rotation transformation of the training data, the neural network can learn the rotation invariance of the target. Aiming at the problem of multi-directional image target and aiming at the problems of large target aspect ratio, large target tilt angle, and changeable direction in the target, we propose to use the tilt detection frame instead of the ordinary rectangular detection frame. Aiming at the problem of dense arrangement of image targets and aiming at a large number of densely arranged targets in the image, a feature refining module is proposed, which can effectively improve the detection performance of the detector for densely arranged targets. The experimental results shows that the proposed algorithm achieves more than 10% on the target detection accuracy with focal length change of 1-10 times. The detection accuracy meets the requirements of practical application.
Democratizing Language Learning using Machine Learning

Most apps available in the market for learning a new language are severely limited in terms of the number of languages users can learn on the platform (“target language”), as well as the language in which users can receive instruction (“source language”). Users are also limited by the fixed sets of lessons or the curriculum provided by these apps. In this work, we present an app framework which allows users free choice of source and target languages, as well as the flexibility in learning any input word, phrase or sentence of their choice using machine translation, whose performance and coverage of new languages is continuously improving. The app provides real-time feedback on the correctness of user pronunciation for any input using a text-based similarity metric, and helps learners practice their pronunciation until they perfect it. The app also provides a conversation platform where intermediate and advanced learners can engage in simple, real-time conversations with a chatbot on topics they are most likely to engage in while learning a new language. The conversation platform uses machine translation and speech-to-text tools to convert user query in any source language into an English query, gets the chatbot response and converts it back to the source language. This simple and novel approach allows users to freely converse in any language they want to practice, while having the chatbot trained only on English language-based corpus. The chatbot also integrates a novel intent classification module that classifies user query into one of several available topics, thereby enabling the chatbot to continue conversation with the user in the same topic. Finally, the chatbot is also capable of integrating search capability for specific queries (e.g., weather) with the help of available public domain resources so that it can provide users with real-time updates for such queries, thus making language learning fun and interesting.
Machine Learning Algorithms to Evaluate Fuzzy Logic Web Services for Monitoring the Real-Time Applications

Most people, particularly those above the age of 65, are vulnerable to serious injury as a result of fall. Usually, falls go unreported and are dismissed as an unavoidable occurrence. In elderly adults, a variety of wearable fall detection systems have now been developed. The majority of these gadgets, however, rely on regional information processors. Wearable technologies employ a variety of methods to detect fall on real time; it emphases fall detection vfuzzification, Java Fuzzy Modeling Language (JFML), and service-oriented architecture, and is based on IEEE 1855-2016. Furthermore, to avoid such mishaps, multiple methodologies were employed that identify a fall employing machine learning algorithms and human movement location information. Wearable acceleration & gyroscope detectors are used to evaluate fuzzy logic web services, to distinguish among the fall & a non-fall, machines learning methods including such k-NN, decision tree, randomized forests, & high gradients boosting are employed. The study will compare real-time practical fall monitoring systems with fuzzy services and machine learning to see which is well suited for real-time fall detection. The suggested fuzzy logic web services will efficiently discriminate between fall and non-fall events in a real-world context, according to study results, with precision, sensitivities, and specificities of 90 percent, 88.89 percent, and 91.67 percent, respectively. Using Machine learning’s randomized forests method scored 99.19 percent, 98.3 percent, and 99.3 percent, respectively.
Real-time Sign Language Recognition using Machine Learning and Neural Network

Languages based on gestures have recently garnered a lot of interest. In sign language, they are one of the nonverbal communication modalities. It is most commonly used by deaf and dumb individuals with vocal or hearing issues in order to convey and talk with one other and normal people without disabilities. The motion, form, and location of hands, along with the body gestures and facial expressions, are used for conveying the meaning in American Sign Language(ASL). ASL translator also aids in the communication between signers and non-signers. The most efficient way in which they can convey and communicate is through using gestures and that is particularly known as Sign language. Hearing-impaired persons find it very complex and a problem to communicate with the normal people that are in the rest of the world, and as a result, they are isolated, that is the humans with speech impediments communicate via gestures, which makes it difficult for them to mix with the able-bodied. Hence, an interpretive system is required to serve as a link between them and individuals who do not understand their sign language. To assist them overcome these obstacles, we provide a project that will be beneficial to them. This project proposes a Machine Learning-based method for recognising hand gestures and translating them into speech/text, as well as the reverse, converting speech/text to gesture.It introduces a conceptual framework that can interpret sign language autonomously, allowing deaf and dumb individuals to convey more readily with one another and with regular people. Recognition systems and motion tracking are two important and crucial areas of study and are still in their infancy.
Cloud Storage and Authenticated Access For Intelligent Medical System

The Internet of Things (IoT) and smart diagnostic implants have improved medical systems by allowing remote access and screening of patients' health conditions from any time and location. Medical IoT devices monitor patients' health and collect data into medical files, which are then saved in the cloud and reviewed by doctors for decision-making.When it comes to data sharing and outsourcing on the Cloud, however, the security and privacy of electronic health records are key concerns. To guarantee security and privacy in a smart healthcare system, this research presents an efficient data storage and access control system. This article adds three layers of security, including a password, a deep learning enabled psychometric slot, and a deep learning-based evaluation. The proposed method has been put to the test, and the results show that it is viable when compared to existing access control schemes.
Disclosure Risk from Homogeneity Attack in Differentially Private Release of Frequency Distribution

Differential privacy (DP) provides a robust model to achieve privacy guarantees in released information. We examine the robustness of the protection against homogeneity attack (HA) in multi-dimensional frequency distributions sanitized via DP randomization mechanisms. We propose measures for disclosure risk from HA and derive closed-form relationships between privacy loss parameters in DP and disclosure risk from HA. We also provide a lower bound to the disclosure risk on a sensitive attribute when all the cells formed by quasi-identifiers are homogeneous for the sensitive attribute. The availability of the closed-form relationships helps understand the abstract concepts of DP and privacy loss parameters by putting them in the context of a concrete privacy attack and offers a perspective for choosing privacy loss parameters when employing DP mechanisms to release information in practice. We apply the closed-form mathematical relationships on real-life datasets to assess disclosure risk due to HA in differentially private sanitized frequency distributions at various privacy loss parameters.
Automatic Co-Registration of Aerial and Satellite Imagery using optimized minimum distance thresholds for matching

Imagery is captured by different satellites, Unmanned Aerial Vehicles (UAV’s), equipment’s or sensors, from different angles and at different time frames. Difference in nature of data, orientations or settings of satellites, equipment’s or sensors may lead to introduction of errors in the imagery. Due to various such errors it becomes difficult for the imagery to be deployed in various remote sensing applications such as fusion of images, change detection panorama creation etc. Hence, we need image registration which geometrically aligns images belonging to the same area. This paper provides a solution for removal of the registration errors automatically for multi temporal and multi resolution imagery, hence facilitating Automatic Image co-registration. Feature extraction utilizes the SURF (Speeded-Up Robust Features). Feature matching is done using Fast Library for Approximate Nearest Neighbours (FLANN) matcher. The work focuses on solving the problems faced in matching of valid key point descriptors of the sensed and reference imagery, particularly the panchromatic satellite imagery. Multiple scenarios for minimum distance thresholds have been visualized for eliminating the bad matches. Very high accuracy of less than a meter in most cases has been achieved for satellite imagery, using the above method of image registration.
An ECC-Based Digital Signature Scheme for Privacy Protection in Wireless Communication Network

Aiming at the security problems of wireless communication network and the shortcomings of Elliptic Curve Digital Signature Algorithm (ECDSA), this paper designed a forward secure digital signature scheme and proved that the scheme has forward security and unforgeability in the random oracle model. Experimental simulations are done in this paper, and the results show that the proposed scheme has the highest efficiency compared with the ECDSA scheme and the three existing forward secure schemes. This scheme not only meets the forward security and protects the users’ privacies but also reduces the requirement of computing power of the user in the wireless communication network.
Study the path planning of intelligent robots and the application of blockchain technology

With the improvement of China's science and technology, robot technology has been widely developed and applied. The most significant feature of China's intelligent robot technology advancement is the combination of embedded systems and robotics. Embedded systems have higher specific integration and lower power consumption. The binocular vision is introduced into the embedded system, and the inheritance and development of the algorithm is facilitated by the transplantation and use of the OpenCV vision library. Compared with the traditional method of using a separate vision chip, the development cost is saved and the application prospect is broad. Such advantages not only can fully meet the real-time nature of the system, but also can simplify the development of control software to a certain extent. The blockchain system using the proof-of-work mechanism can complete the issuance and transfer of digital assets in the presence of untrusted nodes. Different blockchains can obtain different security based on different economic incentives and punishment mechanisms. Among them, the proof of work is still the most secure consensus mechanism in the blockchain. According to the actual needs of embedded intelligent robot path planning, by comparing the advantages and disadvantages of existing algorithms and the practical ability of the external environment, a path planning algorithm based on the combination of A* and artificial potential field method is proposed. In the simulation experiments of this paper, the potential field ant colony algorithm rasterizes the environment, reduces the probability that the resultant force of the robot is zero, and increases the pheromone concentration in the direction of the resultant force, and converts the direction factor of the force into the pheromone factor. The ability to avoid obstacles is improved, the algorithm is prevented from falling into a local optimal situation, and the global optimal path is searched with path constraints (33.799).
M-ary Phase Position Shift Keying Demodulation Using Stacked Denoising Sparse Autoencoders

A deep-learning based detector for M-ary phase position shift keying (MPPSK) systems is proposed in this paper. The major components of this detector include a special impact filter, a stacked denoising sparse autoencoder (DSAE), which was trained in unsupervised learning to extract features from the modulation signals, and a softmax classifier. The features learned by the stacked DSAE were then used to train the softmax classifier to demodulate the received signals into M classes. The architecture presented herein was trained and tested on a simple dataset extended by adding Gaussian noise only. The results from the theoretical analysis and simulation show that the detection performance of the proposed scheme is superior to that of existing detectors.
Secure Data Sharing of Personal Health Records in Cloud Using Advanced Encryption Standard

The Advanced Encryption Standard (AES) is one of the prominent crypto cores that follows a symmetric key approach and offers better security. There need to be a best security practice to safeguard it and also for accessing the data in future whenever needed. An encryption key is created and the doctor is given it. Performing the deduplication scheme over the encrypted data in Public Health Sector consider as the challenging task. The primary objective of this work is to store the data automatically and it will be accessible via a continuous data acceleration process and to avoid deduplication. For the evaluation of the proposed research work metrics of, Time for Encrypting, Time for Decrypting on security over heads in milli-seconds is analyzed. In overall, the deduplication scheme method proposed by the research work provides the better trade-offs between security and privacy to avoid the deduplication data.
Sequential Recommendation Using Deep Reinforcement Learning and Multi-Head Attention

Recommender Systems have become a crucial part of many of our online interactions. From shopping for clothes, planning a trip, or deciding what to watch, recommender systems are aiming to help users navigate the overwhelming amount of options available online. The problem with most of the existing recommender systems is that they treat the recommendation process as a static one and make recommendations according to a fixed greedy strategy. This is a problem because user preferences are dynamic. In this paper, we aim to address this problem by modeling the recommendation problem as a Markov Decision Process (MDP) and solving it using deep reinforcement learning. Furthermore, we use multi-head attention to improve the recommendations. We conduct extensive experiments using the MovieLens real-world dataset and achieve an improvement of 6&#x0025; over the state-of-the-art approach results in terms of precision@20.
Density-Based Geometric One-Class Classifier Combined with Genetic Algorithm

One of the most prospective issues in recent machine learning research is one-class classification (OCC), which considers datasets composed of only one class and outlier. It is more reasonable than the traditional multiclass classification in dealing with problematic datasets or special cases. Generally, classification accuracy and interpretability for users are considered to have a trade-off in OCC methods. A classifier based on hyperrectangle (H-RTGL) can alleviate such a trade-off and uses H-RTGL formulated by the conjunction of geometric rules (called an interval). This interval can form a basis for interpretability since it can be easily understood by the user. However, the existing H-RTGL-based OCC classifiers have the following limitations: (i) they cannot reflect the density of the target class, (ii) the density is considered using a primitive interval generation method, and (iii) there exists no systematic procedure for determining the hyperparameter of the H-RTGL-based OCC classifier, which influences its classification performance. Therefore, we suggest a one-class hyperrectangle descriptor based on density <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mfenced open="(" close=")" separators="|"> <mrow> <mn>1</mn> <mo>−</mo> <mi>H</mi> <mi>R</mi> <msub> <mrow> <mi>D</mi> </mrow> <mrow> <mi>d</mi> </mrow> </msub> </mrow> </mfenced> </math> with a more elaborate interval generation method, including parametric and nonparametric approaches. Specifically, we design a genetic algorithm that comprises a chromosome structure and genetic operators for systematic generation of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mn>1</mn> <mo>−</mo> <mi>H</mi> <mi>R</mi> <msub> <mrow> <mi>D</mi> </mrow> <mrow> <mi>d</mi> </mrow> </msub> </math> through optimization of the hyperparameter. Our study is validated through a numerical experiment using several actual datasets with different sizes and features, and the result is compared to the existing OCC algorithms along with other H-RTGL-based classifiers.
The Metabolism Grey Prediction Model Based on Big Data and Internet of Things Technology

In view of the uncertainty and diversity of the metabolic grey prediction model in the prediction process, resulting in poor prediction effect, a metabolic grey prediction model based on big data and Internet of things technology is constructed. Establish the aerobic metabolic process of human telecontrol and put forward the detection index of aerobic metabolic cycle function in human telecontrol; on this basis, use the metabolic grey prediction model analysis algorithm to determine the active intrusion intention of complex network, establish the intrusion intention attack behavior set function, establish the internal operation architecture under the technology of big data and Internet of things, and realize the construction of metabolic grey prediction model. The experimental results show that the constructed model can realize data prediction, with high confidence level and good effect.