Reviewerly/exHarmony huggingface dataset:
Privacy-Preserving Outsourcing Scheme for SVM on Vertically Partitioned Data

Support vector machine (SVM) is an important technique for data classification. Traditional SVM assumes free access to data. If the data are split and held by different users, for privacy reasons, users are likely unwilling to submit their data to a third party for classification. In this paper, by using additive homomorphic encryption and random transformations (matrix transformation and vector decomposition), we design a privacy-preserving outsourcing scheme for conducting Least Squares SVM (LS-SVM) classification on vertically partitioned data. In our system, multiple data owners (users) submit their encrypted data to two non-colluding service providers, which conduct SVM algorithm on it. During the execution of our algorithm, neither service provider learns anything about the input data, the intermediate results, or the predicted result. In other words, our algorithm is encrypted in the whole process. Extensive theoretical analysis and experimental evaluation demonstrate the correctness, security, and efficiency of the method.
Redefining Text-to-SQL Task as a Machine Translation Problem

Natural language interfaces are gaining popularity as an alternative interface for non-technical users. Natural language interface to database (NLIDB) systems have been attracting considerable interest recently that are being developed to accept user’s query in natural language (NL), and then converting this NL query to an SQL query, the SQL query is executed to extract the resultant data from the database. This Text-to-SQL task is a long-standing, open problem, and towards solving the problem, the standard approach that is followed is to implement a sequence-to-sequence model. In this paper, I recast the Text-to-SQL task as a machine translation problem using sequence-to-sequence-style neural network models. To this end, I have introduced a parallel corpus that I have developed using the WikiSQL dataset. Though there are a lot of work done in this area using sequence-to-sequence-style models, most of the state-of-the-art models use semantic parsing or a variation of it. None of these models’ accuracy exceeds 90%. In contrast to it, my model is based on a very simple architecture as it uses an open-source neural machine translation toolkit OpenNMT, that implements a standard SEQ2SEQ model, and though my model’s performance is not better than the said models in predicting on test and development datasets, its training accuracy is higher than any existing NLIDB system to the best of my knowledge.
An Efficient Resource Allocation Mechanism Using Intelligent Scheduling for Managing Energy in Cloud Computing Infrastructure

We hit the issue of conservation of energy in cloud data centers with the effective utilization of resources used for the completion of tasks in cloud. Here, we consider the virtual machine requirements and task completion time of process. We model the system based on the process priority and task completion with the availability of resources for intelligent scheduling. Based on the scheduling algorithm, the energy efficiency and performance got increased. Then, we can reduce the power and cost of maintenance using the proposed methodology.
Multi-Turn and Multi-Granularity Reader for Document-Level Event Extraction

Most existing event extraction works mainly focus on extracting events from one sentence. However, in real-world applications, arguments of one event may scatter across sentences and multiple events may co-occur in one document. Thus, these scenarios require document-level event extraction (DEE), which aims to extract events and their arguments across sentences from a document. Previous works cast DEE as a two-step paradigm: sentence-level event extraction (SEE) to document-level event fusion. However, this paradigm lacks integrating document-level information for SEE and suffers from the inherent limitations of error propagation. In this article, we propose a multi-turn and multi-granularity reader for DEE that can extract events from the document directly without the stage of preliminary SEE. Specifically, we propose a new paradigm of DEE by formulating it as a machine reading comprehension task (i.e., the extraction of event arguments is transformed to identify the answer span from the document). Beyond the framework of machine reading comprehension, we introduce a multi-turn and multi-granularity reader to capture the dependencies between arguments explicitly and model long texts effectively. The empirical results demonstrate that our method achieves superior performance on the MUC-4 and the ChFinAnn datasets.
Annotation Projection-based Dependency Parser Development for Nepali

Building computational resources and tools for the under-resourced languages is strenuous for any Natural Language Processing task. This article presents the first dependency parser for an under-resourced Indian language, Nepali. A prerequisite for developing a parser for a language is a corpus annotated with the desired linguistic representations known as a treebank. With an aim of cross-lingual learning and typological research, we use a Bengali treebank to build a Bengali-Nepali parallel corpus and apply the method of annotation projection from the Bengali treebank to build a treebank for Nepali. With the developed treebank, MaltParser (with all algorithms for projective dependency structures) and a Neural network-based parser have been used to build Nepali parser models. The Neural network-based parser produced state-of-the-art results with 81.2 Unlabeled Attachment Score, 73.2 Label Accuracy, and 66.1 Labeled Attachment Score on the gold test data. The parser models have also been evaluated with the predicted Part-of-speech (POS)-tagged test data. A statistical POS tagger using Conditional Random Field has been developed for predicting the POS tags of the test data.
Improved Boundary Support Vector Clustering with Self-Adaption Support

Concerning the good description of arbitrarily shaped clusters, collecting accurate support vectors (SVs) is critical yet resource-consuming for support vector clustering (SVC). Even though SVs can be extracted from the boundaries for efficiency, boundary patterns with too much noise and inappropriate parameter settings, such as the kernel width, also confuse the connectivity analysis. Thus, we propose an improved boundary SVC (IBSVC) with self-adaption support for reasonable boundaries and comfortable parameters. The first self-adaption is in the movable edge selection (MES). By introducing a divide-and-conquer strategy with the k-means++ support, it collects local, informative, and reasonable edges for the minimal hypersphere construction while rejecting pseudo-borders and outliers. Rather than the execution of model learning with repetitive training and evaluation, we fuse the second self-adaption with the flexible parameter selection (FPS) for direct model construction. FPS automatically selects the kernel width to meet a conformity constraint, which is defined by measuring the difference between the data description drawn by the model and the actual pattern. Finally, IBSVC adopts a convex decomposition-based strategy to finish cluster checking and labeling even though there is no prior knowledge of the cluster number. Theoretical analysis and experimental results confirm that IBSVC can discover clusters with high computational efficiency and applicability.
Enabling Short-Term Energy Flexibility Markets Through Blockchain

Climate change has put significant pressure on energy markets. Political decisions such as the plan of the German government to shut down coal power plants by 2038 are shifting electricity production towards renewable and distributed energy resources. The share of these resources will continue to grow significantly in the coming years. This trend changes the ways how energy markets work which mandates fundamental changes in the underlying IT infrastructure. In this paper, we propose a blockchain-based solution which enables an economically viable and grid-serving integration of distributed energy resources into the existing energy system. Our blockchain-based approach targets intraday and day-ahead operating reserve markets, on which energy grid operators and operators of distributed energy resources can trade flexibilities within the schedulable energy production and consumption of their resources. By utilizing these flexibilities as an operating reserve, renewable and climate-friendly technologies can contribute to maintaining the grid stability and security of supply while simultaneously creating economically interesting business models for their operators. We propose to define blockchain-based short-term energy markets by utilizing the concept of general-purpose smart contracts and cryptocurrencies. This enables direct and decentralized trading of energy flexibilities without any intermediary or central instance. We demonstrate the feasibility of our approach through an implementation of a prototype of the proposed markets based on the Ethereum blockchain and provide a detailed evaluation of its efficiency and scalability.
Supervised Contrastive Learning for Interpretable Long-Form Document Matching

Recent advancements in deep learning techniques have transformed the area of semantic text matching (STM). However, most state-of-the-art models are designed to operate with short documents such as tweets, user reviews, comments, and so on. These models have fundamental limitations when applied to long-form documents such as scientific papers, legal documents, and patents. When handling such long documents, there are three primary challenges: (i) the presence of different contexts for the same word throughout the document, (ii) small sections of contextually similar text between two documents, but dissimilar text in the remaining parts (this defies the basic understanding of “similarity”), and (iii) the coarse nature of a single global similarity measure which fails to capture the heterogeneity of the document content. In this article, we describe CoLDE : Co ntrastive L ong D ocument E ncoder—a transformer-based framework that addresses these challenges and allows for interpretable comparisons of long documents. CoLDE uses unique positional embeddings and a multi-headed chunkwise attention layer in conjunction with a supervised contrastive learning framework to capture similarity at three different levels: (i) high-level similarity scores between a pair of documents, (ii) similarity scores between different sections within and across documents, and (iii) similarity scores between different chunks in the same document and across other documents. These fine-grained similarity scores aid in better interpretability. We evaluate CoLDE on three long document datasets namely, ACL Anthology publications, Wikipedia articles, and USPTO patents. Besides outperforming the state-of-the-art methods on the document matching task, CoLDE is also robust to changes in document length and text perturbations and provides interpretable results. The code for the proposed model is publicly available at https://github.com/InterDigitalInc/CoLDE .
ID-Based Multireceiver Homomorphic Proxy Re-Encryption in Federated Learning

Data privacy has become a growing concern with advances in machine learning. Federated learning (FL) is a type of machine learning invented by Google in 2016. In FL, the main aim is to train a high-accuracy global model by aggregating the local models uploaded by participants, and all data in the process are kept locally. However, compromises to security in the cloud server or among participants render this process insufficiently secure. To solve the problem, this article presents an identity-based multireceiver homomorphic proxy re-encryption (IMHPRE) scheme that utilizes homomorphism operations and re-encryption to provide improved encrypted-data processing and access control. When this scheme is employed, participants can directly use public identities for encryption. The IMHPRE scheme is also secure against the chosen-plaintext attacks. Comparison results indicated that the IMHPRE outperforms its counterparts because it allows a cloud server to perform model aggregation on re-encrypted models for multiple receivers.
Attitude Monitoring Algorithm for Volleyball Sports Training Based on Machine Learning in the Context of Artificial Intelligence

With the development of artificial intelligence technology and information technology, the posture of volleyball training is becoming increasingly strict. By analyzing the dynamic training posture monitoring algorithm, the posture information of the human body can be directly obtained, which enables more efficient management of volleyball sports training. This paper aims to study how to monitor volleyball training posture and give suggestions based on machine learning in the context of artificial intelligence. The traditional method of manually detecting volleyball training postures is too subjective and cannot be used to judge the movements. Therefore, this paper proposes an algorithm for human posture monitoring and studies human posture recognition. Human gesture recognition has been widely used in many fields. The experimental results in this paper show that the corrected serve deviation rate of five volleyball players is 13.1% at the highest and 11.3% at the lowest after the traditional manual visual monitoring. The highest error is 0.70 m and the lowest is 0.63 m. The overall error is high. The corrected service deviation rate of the machine learning-based attitude monitoring algorithm is 3.5% at the highest and 2.7% at the lowest. The highest error is 0.24 m and the lowest is 0.19 m. The overall error is much lower than the former. This also shows that the posture monitoring algorithm based on machine learning can effectively detect the movement of volleyball players. This enables athletes to correct their mistakes in a timely manner, improve training efficiency, and improve their own strength.
The Teaching Capacity of Industry Characteristic Counselors Based on Data Fusion and Random Forest Algorithm

The good development of industry-specific universities (hereinafter referred to as characteristic universities) promotes the optimal allocation of national university resources and moreover promotes the development of China’s education, while counselors, as important teaching and research staff, have a very close connection with the development level of the school, directly reflecting the quality and level of education of the school and determining the height of its educational development. Talent is the first productive forces, counselors as teaching talents, and is essential to the construction of industry specialty universities. The evaluation index system of teaching ability of counselors can effectively guide counselors toward the path of specialization, scientifically realize career planning, and maximize the achievement of the general goal of the characteristic school they are in. This article aims to assess the teaching capacity of counselors and targeted training capabilities. The counselor’s ability is reflected in the teaching of teaching, completing teaching objectives and cultivates student’s hobbies. In this paper, through investigating the cultivation methods of different industries, the cultivation of counselor’s ability is to assess consensus and establish multilevel teachers’ teaching capacity cultivation system. The results show that the system can effectively improve the teaching capacity of young teachers and increase 20% of student’s satisfaction; however, it also shows that the professional competence of counselors still needs to be improved.
On the Classification of Modulation Schemes Using Higher Order Statistics and Support Vector Machines

The recognition of modulation schemes in military and civilian applications is a major task for intelligent receiving systems. Various Automatic Modulation Classification (AMC) algorithms have been developed for this purpose in the literature. However, classification with low computational complexity as well as reasonable processing time is still a challenge. In this paper, a feature-based approach along with various classifiers is employed based on statistical features as well as higher-order moments and cumulants. An over-the-air (OTA) recorded dataset consisting of four analog and ten digital modulation schemes are used for testing the proposed method at 0–20 dB SNR. The overall accuracy for quadratic Support Vector Machine (SVM) is found to be as high as 98% at 10 dB. The comparison of the results with other AMC papers published in the literature indicates that the proposed method present higher accuracy, especially for realistic channel induced OTA dataset.
Research on Dynamic Spectrum Allocation Algorithm Based on Cyclic Neural Network

Due to the wide application of cognitive wireless network, the network structure is becoming more and more complex. It is difficult to establish the corresponding mathematical model to simulate the high complexity network environment. The algorithm based on recurrent neural network in deep reinforcement learning can effectively solve this problem. In addition, with the rise of deep learning in recent years, the combination of reinforcement learning and deep learning shows excellent ability in dealing with complex problems and data operation. This paper is aimed at studying dynamic spectrum allocation based on cyclic neural network. This paper briefly introduces MATLAB, sets up the network environment of algorithm simulation, then analyzes the overall performance of the improved genetic algorithm, and explores the influence of genetic algorithm-related parameters and network environment-related parameters on the performance of the algorithm. The results show the improved genetic algorithm. The network efficiency can be improved by about 2%, but the spectrum switching frequency can be reduced by 69%. When the number of primary users in the network is large, the network benefit of improving the genetic algorithm is superior to the other two algorithms. In addition, when the crossover probability is 0.6 and 0.1, the fitness value is higher than the crossover probability of 0.9 and 0.5; the interference of authorized users in the network initially has less impact on the secondary user.
Robust and Efficient Shoe Print Image Retrieval Using Spatial Transformer Network and Deep Hashing

In recent years, great progress has been made on the topic of shoe print image retrieval. However, it still remains a big challenge to accurately retrieve well-matched shoe print images from a huge database in real time. Deep hashing method has been proved to be effective in large-scale image retrieval in many applications. Output of deep hash network can be represented as a binary bit hash code, which helps to reduce storage space and retrieval time. Existing shoe print image retrieval methods have poor performance in instance retrieval because of shortage of classification information, sufficient sample information and feature quantization information. In order to overcome the problem, we put forward an end-to-end network to learn short deep hash codes. The learned hash code preserves the classification and small sample information very well. Moreover, due to the fact that STN (Spatial Transformer Network) block is simultaneously embedded into the hash network to enhance the retrieval ability for rotated shoe print images, the problem of rotation misalignment can be solved and the retrieval accuracy is improved. Furthermore, in order to make better use of class label information, we presented a new joint loss function. This loss function helps the network map both images’ classification information and similarities into hash codes and reduce quantitative loss. In addition, we used triple labels to alleviate sample imbalance problem. Experiments on database including 10,500 shoe print images show that our proposed method can improve the retrieval performance. The proposed approach can yield a mAP (mean Average Precision) of 0.83 and a recall of 0.35, which demonstrates the discriminatory power of the learned hash codes in shoe print image retrieval application.
A Facilitation System for Arabic Foreigners in India Using the Web and Android System

In this paper, we have developed an android facilitation application that intended Arabic foreigners who come to the Republic of India for study, treatment, or tourism purposes. They may need help due to the difference in some languages. Also, they have lack knowledge of some places and how to move to India and find some difficulties in some social services. Therefore, this study aimed to solve the problem, assistance, and guidance of Arabic foreigners who are new arrivals or even pre-residents in India. The data of the Indian city of Aurangabad was collected and filled as a primitive sample in terms of universities, specialties, tuition fees, residential neighborhoods, medical facilities, specialized doctors and clinics, market centers, and shopping with English and Arabic languages. The study also dealt with some linguistic problems to facilitate communication between English, Arabic, and Hindi. Some foreigners find it difficult to pronounce the language the first time. The application also receives notifications about news and events in the city of India. The application is connected with a website and MySQL database to get the information for registered users.
Improving Performance Using Hybrid Framework Iot Communication In Cloud Computing

Cloud computing is a new service component that allows any customer to connect to a high-end computer network. Because many cloud computing platforms provide services to a large number of users who are considered trustworthy, they can be the victims of many online attacks. We will need more rules or patterns if we want strict security, which means we will need more calculation resources in line with security quality. Because enterprise applications are deployed in the cloud to keep frame costs low, part of the role of the cloud server in continuous research directs the cloud server step by step to accommodate large data counts. Because the cloud server collects and updates a lot of data on a regular basis, it should be monitored to keep track of the problem and to avoid slow responses in a bad system situation. To investigate the flow of data and stable medical data to obtain health care information.
Artificial Intelligence and Digital Pathology Synergy: For detailed, accurate and predictive analysis of WSIs

Pathology is the most important branch of Medical Science which fundamentally concerns the origin, cause, and nature of the disease. This domain contributes the most important part of the diagnostic infrastructure. The pathological examination includes examining, Tissues, Organs, Body fluids. In this examination, the tissue is fixed on a glass slide by certain procedures and then observed under a microscope for disease. Now with technological advancement, the traditional pathological operation is getting evolved and emerged as Digital pathology. Digital pathology comprises the digitalization of Histopathological slides using a Whole-slide scanner which is a microscope under robotic and computer control and analysing these digitalized images or whole-slide image using a Computational approach. This digitalization creates a high-resolution, enhanced pixel image of pathological specimen that ultimately generates a tremendous amount of data up to gigabytes per biopsy. Because of algorithmic advancement and more convenient computing power, in the past few decades, Artificial Intelligence and machine learning have spread their roots into healthcare and clinical applications. And artificial intelligence and digital pathology synergy is one of the examples of these two fields intersection. This synergy will have majestic results in Diagnostic, prognostic, and predictive analysis of Whole-slide images.