Reviewerly/exHarmony huggingface dataset:
Comparative Evaluation of Semantic Similarity Upon Sentential Text of Varied (Generic) Lengths

Semantic similarity plays a vital role in natural language processing, information retrieval, text mining, question and answering system, text-related research, biomedical informatics, and plagiarism checking. Measuring similarity between sentences means estimating the degree of closeness in meaning between these sentences. Very recently, NLP research has observed much-inclined shift from word-lexicon and syntactic feature-based techniques to structural feature-based techniques of exploring NLP semantics. Rather, no stone is unturned by NLP community working on computation of text semantic similarity to formulate supporting feature spaces, say, word-to-word co-occurrences, lexical corpora and natural language grammar rule-based word order vectors as well as semantically parsed structures. The most popularly used WordNet lexical dictionary was exploited as hierarchical structures (semantic trees) to fetch all combinations of noun and verb families of word phrases, predominantly occupying free texts. In this paper, the computation of text semantic similarity is addressed by devising a novel set of generic similarity metrics based on both, word-sense of the phrases constituting the text as well as the grammatical layout and sequencing of these word phrases forming text with sensible meaning. The experiments performed on benchmark datasets created by Li and group explore semantically parsed dependency structures in feature extraction step and achieve very promising values of semantic closeness within sentence pairs. The model’s performance is indicated achieving highest value of Pearson’s correlation coefficient (0.89) with mean-human similarity scores against those obtained through closely competent structured approach models. However, these very promising results are contributed by word-sense similarity component of sentential similarity measure. A future scope of work is also incepted that shall also improve upon dependency (grammatical relations) component to raise the text similarity performance metric.
Interferogram blind denoising using deep residual learning for phase-shifting interferometry

The interferogram containing the noises often affects the accuracy of phase retrieval, leading to the degradation of the phase imaging quality. To address this issue, a new interferogram blind denoising (IBD) method based on deep residual learning is proposed. In the presence of unknown noise levels, during the training, the deep residual convolutional neural networks (DRCNN) in the IBD approach is able to remove the latent clean interferogram implicitly, and then gradually establish the residual mapping relation in the pixel-level between the interferogram and the noises. With a well-trained DRCNN model, this algorithm can deal not only with the single-frame interferogram efficiently but also with the multi-frame phase-shifted interferograms collaboratively, while effectively retaining interferogram features related to phase retrieval. Simulation and experimental results demonstrate the feasibility and applicability of the proposed IBD method.
Milton Babbitt, “The Widow’s Lament in Springtime” (1951)

This piece for voice and piano unfolds in four distinct contrapuntal lines. Each line is organized the same way—with the twelve tones arranged to feature particular intervals and trichords—and the lines are coordinated with each other to produce twelve-tone harmonic aggregates. The underlying twelve-tone structure is based on a trichordal array.
Data Mining for Solving Medical Diagnostics Problems

The article solves the problem of creating a software package for computer diagnostics of gastritis. The patient examination indicators and their diagnoses are used as input data. To successfully solve this problem, a logical approach to data analysis is being developed, which allows us to find the patterns necessary for high-quality diagnostics. These laws are identified based on the data provided by specialists and include the results of patient examinations and the existing experience in medical practice in making a diagnosis. Systems of multivalued predicate logic are used for expressive data representation. An algorithm is proposed that implements and simplifies the approaches under consideration. As a result, the developed software package selects the most suitable types of the disease with a predetermined accuracy based on the data of patient diagnostics. If it is not possible to make a diagnosis with a given accuracy based on the results of the examination, then either the accuracy of the decision changes, or it is proposed to undergo an additional examination.
Particle Swarm Network Design for UCAV Intelligence System Path Planning

In military battle, the unmanned combat aerial vehicle (UCAV) plays a critical role. The UCAV avoids the fatal military zone as well as radars. If there is just a narrow path between the defensive areas, it is dan-gerous. It chooses the quickest and safest path. The balance evolution technique is used to improve the path planning of UCAV in this study, which results in a novel artificial bee colony. To regulate the position of a swarm of UCAVs, a particle swarm network is used to communicate between the UCAVs in the swarm. According to simulation data, the particle swarm network technique is more efficient than the ABC ap-proach. The intelligence system is taught via an artificial neural network.
Layered feature representation for differentiable architecture search

Differentiable architecture search (DARTS) approach has made great progress in reducing the computational costs of designing automatically neural architectures. DARTS tries to discover an optimal architecture module, called as the cell, from a predefined super network containing all possible network architectures. Then a target network is constructed by repeatedly stacking this cell multiple times and connecting each one end to end. However, the repeated design pattern in depth-wise of networks fails to sufficiently extract layered features distributed in images or other media data, leading to poor network performance and generality. To address this problem, we propose an effective approach called Layered Feature Representation for Differentiable Architecture Search (LFR-DARTS). Specifically, we iteratively search for multiple cell architectures from shallow to deep layers of the super network. For each iteration, we optimize the architecture of a cell by gradient descent and prune out weak connections from this cell. Meanwhile, the super network is deepen by increasing the number of this cell to create an adaptive network context to search for a depth-adaptive cell in the next iteration. Thus, our LFR-DARTS can obtain the cell architecture at a specific network depth, which embeds the ability of layered feature representations into each cell to sufficiently extract layered features of data. Extensive experiments show that our algorithm solves the existing problem and achieves a more competitive performance on the datasets of CIFAR10 (2.45% error rate) , fashionMNIST (3.70%) and ImageNet (25.5%) while at low search costs.
Automatic flagging of AI segmentation errors in computational pathology

Active Learning (AL) is an artificial intelligence (AI) training paradigm that improves training efficiency in cases where labeled training is hard to obtain. In AL, unlabeled samples are selected for annotation using a bootstrap classifier to identify samples whose informational content is not represented in the current training set. Given a small number of samples, this optimizes training by focusing annotation on “informative” samples. For computational pathology, identifying the most-informative samples is non-trivial, particularly for segmentation. In this work, we develop a feature-driven approach to identifying informative samples. We use a feature extraction pipeline operating on segmentation results to find “outlier” samples which are likely incorrectly segmented. This process allows us to automatically flag samples for re-annotation based on architecture of segmentation (compared with less robust confidence-based approaches). We apply this process to the problem of segmenting oral cavity cancer (OCC) H&E stained whole-slide images (WSIs), where the architecture of OCC tumor growth is an aggressive pathological indicator. Improving segmentation requires costly annotation of WSIs; thus, we seek to employ an AL approach to improve annotation efficiency. Our results show that, while outlier features alone are not sufficient to flag samples for re-annotation, we can identify some WSIs which fail segmentation.
Preparation of a Portable and Simple Educational Tool for Learning Lewis Structures

A portable and simple educational tool was prepared for learning Lewis structures in chemistry. This tool consisted of thirteen round magnetic chips (25 mm o.d.) , twenty-five smaller (18×8 mm) and two larger (26×10 mm) square ones indicating an element of an atom, an electron pair or bond, and an ionic balance, respectively, on the front and back sides. All these chips were packed in a mini ziplock bag (5×7 cm) for portable use. More than fifty Lewis structures of molecules and ions in total were possible to be constructed by putting suitable magnetic chips regularly on a whiteboard. When this tool was used in an online chemistry class for first-year students, it was inferred that it helped understanding Lewis structures of chemical species from the result of questionnaires for the students.
Combined Spatial Features Recognition Method for Similar Video Action Automatic Classification

research-article Share on Combined Spatial Features Recognition Method for Similar Video Action Automatic Classification Authors: Ling Wang School of Computer Science, Northeast Electric Power University, China School of Computer Science, Northeast Electric Power University, ChinaView Profile , Yuanhao Mei School of Computer Science, Northeast Electric Power University, China School of Computer Science, Northeast Electric Power University, ChinaView Profile , Shiru Gao School of Computer Science, Northeast Electric Power University, China School of Computer Science, Northeast Electric Power University, ChinaView Profile , Tiehua Zhou School of Computer Science, Northeast Electric Power University, China School of Computer Science, Northeast Electric Power University, ChinaView Profile Authors Info & Claims AICCC '21: 2021 4th Artificial Intelligence and Cloud Computing ConferenceDecember 2021 Pages 1–6https://doi.org/10.1145/3508259.3508260Online:17 March 2022Publication History 0citation18DownloadsMetricsTotal Citations0Total Downloads18Last 12 Months18Last 6 weeks4 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 Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Cyclic Transfer Learning for Recommender Systems with Heterogeneous Feedbacks

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)Cyclic Transfer Learning for Recommender Systems with Heterogeneous FeedbacksXuelian Ni, Fei Xiong, Yutian Hu, Shirui Pan, Hongshu Chen, and Liang WangXuelian Ni, Fei Xiong, Yutian Hu, Shirui Pan, Hongshu Chen, and Liang Wangpp.567 - 575Chapter DOI:https://doi.org/10.1137/1.9781611977172.64PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Transfer learning uses auxiliary domains to help complete learning tasks of the target domain. However, the combination of recommendation and transfer learning often has two problems. One is that it's difficult to find an auxiliary domain which is highly related to the target domain. The other is that useful information in auxiliary domains cannot be fully utilized. To make use of the knowledge in auxiliary domains as much as possible, this paper proposes a cyclic transfer learning method which can transfer the shared knowledge in the auxiliary domain and target domain multiple times. Combining this method with recommendation, this paper presents a recommendation framework based on heterogeneous feedbacks and cyclic transfer learning (HCTL-Rec). By studying the relationship between different behaviors of users, this paper proposes two specific recommendation algorithms which combine the novel framework with two auxiliary domains. One is to use users' binary attitude information as an auxiliary domain to better represent users' ratings. The other is to use users' trust relationship as an auxiliary domain and make social recommendation. Experiments are carried out on two real-world datasets with trust relationship. The results show that recommendation quality of the two specific algorithms can achieve significant improvement compared with other state-of-the-art algorithms and can effectively relieve the cold-start problem. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-717-2 https://doi.org/10.1137/1.9781611977172Book Series Name:ProceedingsBook Code:PRDT22Book Pages:1-737
Readability of Websites for the Novel Coronavirus Disease (COVID-19): A Case Study of India

The World Wide Web has become one of the most important resources for the large part of the population in searching healthcare information. The available COVID-19 information on Websites should be reliable and accurate in order to spread awareness. This has become all the more important in times like COVID-19, pandemic situation when it is necessary to create awareness about the disease to large population in a country like India and also provide updated information and status of the situation. However, readability poses barrier for many people. In this paper, readability of online information related to the novel coronavirus disease (COVID-19) is evaluated using online readability tool. It was found that the readability of content addressing COVID-19 is satisfactory. But the absence of multilanguage option in majority of Websites under study forms another barrier in information dissemination.
The Impact of Virtual Reality on Gaming Experience: A Perspective from Half-Life on Steam

The Covid-19 outbreak affected the operations of various businesses due to the temporary closure of several industries. Virtual Reality (VR) technology has experienced a surge in demand. As a new form of human-computer interaction, VR introduces new opportunities for game development. In 2020, the global market for VR games was estimated at $15.81 billion and expected to grow at a compound annual growth rate of 18.0% from 2021 to 2028. Compared with traditional computer games, the immersive nature of VR games and the use of VR headsets bring new experiences to players. A few studies of players' feedbacks and concerns for VR were carried out to enable game developers to better take of the growing VR market. Nevertheless, research conducted on the influence of VR on player reviews data uploaded in the community was limited. Thus, this paper analyzes the impact of VR on the player community by carrying out an analysis of the Half-Life series. The Half-Life series contains three generations: Half-Life (1998), Half-Life 2 (2004), and Half-Life Alyx (2020), which are available on Steam. Steam is a video game digital distribution platform maintained by Valve Corporation. In this study, we first collected the dataset of Half-Life series reviews available on Steam. Notably, we collected a new review dataset of the Half-Life series from Steam, including approximately 60,000 Half-Life 2 with its two downloaded contents (non-VR edition) reviews and 40,000 Half-Life: Alyx (VR edition) reviews up to June 2021. Next, we used the Latent Dirichlet Allocation (LDA) model, and three experts were involved in analyzing these topics to categorize reviews with 11 topics of Half-Life: Alyx. We also detected the pivotal insight that most game players are concerned with only five topics of fundamental game elements between two games. Furthermore, players' positive ratings were compared on these five topics between two games via hypothesis testing to explore the impact of VR technology on video games. Significantly, the p-value of our two-sample t-test reveals the fact that VR has no significant impact on these fundamental game elements.
Vertical Fusion: A Distributed Learning Approach for Vertically Partitioned Data

Over the last couple of years, distributed learning has gained tremendous focus primarily because of the exponential rise in the computing capabilities of handheld devices and their ability to generate enormous data. However, the majority of the existing distributed learning algorithms focus on horizontally partitioned data, where each client has a subset of the entire data containing all the features and their corresponding labels. In this paper, we present a communication-efficient approach to learn from data that is partitioned vertically, i.e., each client holds a subset of the dataset’s attributes. The proposed algorithm tries to find the distributions of each feature residing on the client, and the server trains a global model on the sample data generated from these distributions. Our experiments on multiple datasets show that the proposed approach can achieve accuracies similar to that of a centralized learning setup without transmitting the actual data to the server and using only one communication round.
Research on Data Privacy Protection in Power Industry Control System

Under the condition of network and information, the important data in the power Industrial Control System (ICS) is suffering more and more serious threat and destruction. It is necessary to further strengthen the protection of data privacy in the system, so as to effectively ensure the healthy and stable operation of the power ICS. On account of this, this paper first analyses the necessity of data privacy protection of power ICS, then studies the practical needs of data privacy protection of power ICS. In addition, data privacy protection technology and data privacy protection form of power ICS are analyzed. Finally, this paper gives the data privacy protection strategy for power industrial control network.
From Quantum Fuzzing to the Multiverse: Possible Effective Uses of Quantum Noise

Quantum noise is seen by many researchers as a problem to be resolved. Current solutions increase quantum computing system costs significantly by requiring numerous hardware qubits to represent a logical qubit to average the noise away. However, despite its deleterious effects on system performance and the increased costs it creates, it may have some potential uses. This paper evaluates those. Specifically, it considers how quantum noise could be used to support the fuzzing cybersecurity and testing technique and AI techniques such as certain swarm artificial intelligence algorithms. Fuzzing is used to identify vulnerabilities in software by generating massive amounts of input cases for a program. Quantum noise provides an effective built-in fuzzing capability that is centered around the actual answer to a computation. These same phenomena, of clustered and centered fuzz-noise around the answer of an operation, could be similarly useful to AI techniques that can make effective use of lots of point values for optimization. Effectively, by concurrently considering the ‘multiverse’ of possible results to an operation, created by compounding noise, more beneficial solutions that are proximal to the actual result of an operation can be identified via testing quantum noise points with an effectiveness algorithm. Both of these potential uses for quantum noise are considered herein.
Intention Adaptive Graph Neural Network for Category-Aware Session-Based Recommendation

Session-based recommendation (SBR) is proposed to recommend items within short sessions given that user profiles are invisible in various scenarios nowadays, such as e-commerce and short video recommendation. There is a common scenario that user specifies a target category of items as a global filter, however previous SBR settings mainly consider the item sequence and overlook the rich target category information. Therefore, we define a new task called Category-aware Session-Based Recommendation (CSBR), focusing on the above scenario, in which the user-specified category can be efficiently utilized by the recommendation system. To address the challenges of the proposed task, we develop a novel method called Intention Adaptive Graph Neural Network (IAGNN), which takes advantage of relationship between items and their categories to achieve an accurate recommendation result. Specifically, we construct a category-aware graph with both item and category nodes to represent the complex transition information in the session. An intention-adaptive graph neural network on the category-aware graph is utilized to capture user intention by transferring the historical interaction information to the user-specified category domain. Extensive experiments on three real-world datasets are conducted to show our IAGNN outperforms the state-of-the-art baselines in the new task.
Lightweight ConvNet Model for American Sign Language Hand Gesture Recognition

Deaf and hard-of-hearing persons practice sign language to converse with one other and with others in their community. Even though innovative and reachable technology is evolving to assist persons with hearing impairments, there is more scope of effort to be achieved. Computer vision applications with machine learning procedures could benefit such persons even more by allowing them to converse more effectively. That is precisely what this chapter attempts to do. The authors have suggested a MobileConvNet model that could recognise hand gestures in American Sign Language. MobileConvNet is a streamlined architecture that constructs lightweight deep convolutional neural networks using depthwise separable convolutions and provides an efficient model for mobile and embedded vision applications. The difficulties and limitations of sign language recognition are also discussed. Overall, it is intended that the chapter will give readers a thorough overview of the topic of sign language recognition as well as aid future research in this area.
Health Condition Assessment of Hydraulic System Based on Cloud Model and Dempster–Shafer Evidence Theory

Hydraulic transmission systems are widely used in industry because of their high output power and compact structure. To cope with the ambiguity and uncertainty in the process of hydraulic system health monitoring, this paper adopts the combination of cloud model and Dempster–Shafer evidence theory for multi-sensor data fusion from three levels: data layer, feature layer, and decision layer, which effectively avoids the problem of high conflict of evidence in Dempster–Shafer theory and completes the assessment of health status of a complex hydraulic system. Firstly, the cloud parameters are calculated to establish the expert knowledge base. Secondly, the membership matrix is used to obtain the basic probability assignments of the evidence. Then, the fusion decision of combining the same type of sensors with evidence iterations is used to improve the efficiency of fusion, and finally, Dempster's rule is performed to obtain the hydraulic system health status assessment results. The feasibility and effectiveness of this method are verified on a real data set.
The Customer Vendor Integration (CVI)

The CVI is one of the most critical and important activities of the conversion process. This chapter provides the reports to be executed and the configuration to be done to compete the CVI. This chapter provides guidance on business partner conversion activities. Refer to SAP Note 2265093 to do the complete the CVI synchronization.