Reviewerly/exHarmony huggingface dataset:
Pushing Biological Keywords Based on Apriori Algorithm

Nature is the source of inspiration for innovation design. In order to obtain valuable biological knowledge aiding for the innovative design of products, this paper proposes a method to push biological keywords based on the Apriori algorithm to effectively obtain biological information related to engineering design goals. Firstly, the biological information is retrieved based on the engineering keywords of the engineering design background. Secondly, it is analyzed based on the engineering and biological knowledge features. Finally, combined with the Apriori algorithm, the retrieved biological information is preprocessed, frequent itemsets are mined, and association rules are generated. The related knowledge between engineering and biology is acquired. Some biological keywords with high correlation with engineering keywords are pushed out.
Research On Student Comprehensive Quality Management Information System Based On B/S Architecture

Management information system plays an important role in the management of university students. Combined with the research and development of “student comprehensive quality management information system”, this paper makes an in-depth study on the design and development of student comprehensive quality management system, and provides some references for the scientific management of university students.
Improving Object Detection with Multiple Knowledge Distillation

With the rapid development of deep learning, object detection based on deep convolutional neural networks has achieved remarkable success at its fast speed. Due to the increasing complexity of the object detection training task, the deep convolutional neural network model finally obtained is also relatively large. However, Large models are usually not suitable for online deployment, on the one hand, computing resources used up big, slow speed of response is on the other hand. In order to transfer the knowledge learned from complex model or integration of multiple models to another lightweight model, namely model compression, we propose a new object detection algorithm based on knowledge distillation, called MKD-OD, which makes the model lightweight and easy to deploy without losing performance In this paper, when training and optimizing the student networks, we not only use the soft target output of the teacher networks as knowledge, but also use the structural relations between the layers of the teacher network as knowledge. We also introduce the attention mechanism, focusing on the attention feature maps of the hidden layers of the student and teacher networks. Our combined experience with different distillation configurations in various data sets and convolutional neural network structures shows consistent improvement in the precision and speed trade-offs of our proposed model.
Comparing Neuroevolution Algorithms Using Self Driving Car

A simple neural network defines the movement and behavior of the entities, and its mutation and survival skills are decided using genetic algorithms. Different types of neural networks are applied to identify which worked the best. In this research, using the platform Unity, a self-driving car was trained to avoid obstacles using Neural Networks and genetic algorithms. The algorithms used are Feed-Forward neural network, FTWENN (Fixed Topology Weight Evolution Neural Network) and NEAT (Neuro Evolution of Augmenting Topologies). In genetic algorithms, mutations and crossovers are used to find the car with the best fitness score. The mutation is used to minimize the computations needed to be done to find the algorithm. Crossovers are used to maintain diversity. Unity is used to train the self-driving car to get the best possible fitness score by dynamically modifying the algorithms.
Automated Sinhala News Platform Based on Machine Learning and Deep Learning

Over the years, newspapers are taken as a major activity in citizens. Due to digitalization, online newspapers get famed, and eventually, it seems to be an ideal adaptation for their busy lifestyle. While there are several Sinhala News platforms, nonetheless, provide an advance and better user experience similar to English news platforms like google news. To fill this gap the proposed news platform, dispense personalized news recommendation based on user behaviors and user emotions. To replace the manual news categorization, the proposed system is armed with automatic news categorization. An automatically unnecessary comment removing feature was also added to the proposed system as an extra feature. The system implementation is based on deep learning, machine learning, NLP, sentimental analysis, and image processing techniques where it can provide a better user experience and a new experience.
Track 2 Using the Web for the Public

The Web can be a great tool for bibliographic instruction, publicizing a library’s collection and increasing its use. It is also invaluable for serials-related reference work. This session looked at how public services librarians and other serialists with similar responsibilities can make the Web work for them. Participants created a professional or departmental home page that could be used in serials-oriented public services. Techniques covered included using various search engines, embedding third-party search engines into a Web page, using lists and tables, simple forms and some of the newer and less common HTML tags. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-342-9678. E-mail address: getinfo@haworth.com]
AI-Based Enhanced Time Cost-Effective Cloud Workflow Scheduling

In this paper, two scheduling algorithms are presented, namely, time-constrained early-deadline cost-effective algorithm (TECA) to schedule these time-sensitive workflows at the lowest cost and versatile time-cost algorithm (VTCA) which consider both time and cost constraints; these algorithms considerably enhance the earlier algorithms. TECA schedules activities to be completed as soon as possible and optimizes the costs in resource provisioning. VTCA supports quality of service (QoS)-based scheduling, keeping a balance between completion time and cost for the selected QoS level. Both algorithms schedule tasks of the same height within the minimum completion time (using Max-Min algorithm). The tasks get scheduled on new resources only when their completion times are more than the calculated minimum completion times for the given resource. CloudSim-based results show that our algorithms minimize completion time better than other popular algorithms, in addition to reducing costs. The modeling for costs satisfies the criteria of earliest completion time.
Value of Blockchain in Rule of Law

Blockchain is considered as a new technology to reshape the world, which characterized by anonymity of subject, intelligence of transaction, decentralization of management, autonomy of on-chain rules, data immutability, transparency and symmetry of information, and automation of incentive (or reward and punishment) from the perspective of law. The significance of “blockchain + rule of law” is mainly the technical credit, trust transfer, new consensus mechanism, the coupling effect of code norms and legal norms.
DIGDH: A Novel Framework of Difference Image Grafting Deep Hiding for Image Data Hiding

Data hiding is the technique of embedding data into video or audio media. With the development of deep neural networks (DNN), the quality of images generated by novel data hiding methods based on DNN is getting better. However, there is still room for the similarity between the original images and the images generated by the DNN models which were trained based on the existing hiding frameworks to improve, and it is hard for the receiver to distinguish whether the container image is from the real sender. We propose a framework by introducing a key_img for using the over-fitting characteristic of DNN and combined with difference image grafting symmetrically, named difference image grafting deep hiding (DIGDH). The key_img can be used to identify whether the container image is from the real sender easily. The experimental results show that without changing the structures of networks, the models trained based on the proposed framework can generate images with higher similarity to original cover and secret images. According to the analysis results of the steganalysis tool named StegExpose, the container images generated by the hiding model trained based on the proposed framework is closer to the random distribution.
예비초등교사들의 실습경험 분석을 위한 이론적 분석틀 탐구 : 문화적 학습 담론을 중심으로

This study is a discourse study on what kind of learning pre-primary elementary school teachers culturally acquire through practical experience. Cultural learning is a German educator, Ch. Based on Wolf's theoretical discussion, the culture in which humans live their lives is structured into five key mechanisms: physicality, performance, mimesis, rituals, and images, assuming that various perceptions and learning are revealed empirically. Therefore, it is believed that cultural learning discourse can provide a framework for analyzing this learning culture as the practical experience of pre-primary elementary school teachers can extend directly to the field and to the competency. Therefore, it theoretically discussed what mechanisms the practical experience in the curriculum can be interpreted. Pre-service teachers grow through traditional and ritual hands-on experiences before being given the title of a teacher. Therefore, their cultural learning process can lead to a change in enhancing teacher competency required in social and educational aspects in the future, beyond simply revealing the strengths and weaknesses of the curriculum. Therefore, it is believed that the cultural interpretation of their learning culture will provide a foundation for re-examining the cornerstone of elementary education. Furthermore, it is expected that cultural learning can be provided from a new perspective to examine the various learning cultures of pre-primary elementary school teachers.
Low-cost lightweight-client twin-field quantum key distribution network with wavelength division multiplexing

In recent years, quantum communication technology has gradually entered the practical stage. Multiple quantum key distribution (QKD) networks have been built and tested all over the world. However, quantum communication networking mode always evolves from a point-to-point network to a many-to-many network. The major challenges in quantum communication are secret key rate, distance, cost, and size of QKD devices. We proposed a low-cost, lightweight-client, twin-field QKD network with wavelength division multiplexing (WDM). The proposed implementation adopts polarization multiplexing, time-division multiplexing, and WDM technology to solve all these major challenges and implement a many-to-many dynamic communications network. Furthermore, our scheme can effectively reduce the cost to less than half of that of current QKD networks and has reference significance for large-scale deployment of QKD networks.
Crime Reporting and Perceived Effects on its Victims: A Case Study of Ekpoma

This study focused on crime reporting and perceived effects on its victims: a case study of Ekpoma. Crime is a universal social problem which goes beyond the general notion of theft, murder, assault, rape, fraud, kidnapping and other unwholesome behaviour. This research work posits that whatever definition one gives to crime, the concept suggests a decline of public security and steady descent into social chaos. The research work argues that crime reporting and prevention must dovetail into the entire region of security management. The research work concludes by maintaining the true security goes beyond the provision of modern electronic equipment and increased police surveillance, etc, but involves much more of social justice, equity, employment opportunities in the society at large. Two major theories were use: Strain and Conflict Perspective Theory. Proffer suggestions were made on how the problem of crime reporting and victimization can be managed in Nigeria.
Efficient Neural Network Architectures

Designing efficient neural network architectures is a widely adopted approach to improve efficiency, besides compressing an existing deep neural network. A CNN (Convolutional Neural Network ) model typically consists of convolution layers, pooling layers, and fully-connected layers, where most of the computation comes from convolution layers. For example in ResNet-50, more than 99% multiply-accumulate operations (MACs) are from convolution layers. Therefore, designing efficient convolution layers is the core of building efficient CNN architectures. This chapter first describes the standard convolution layer and then describes three efficient variants of the standard convolution layer. Next, we present three representative manually design efficient CNN architectures, including SqueezeNet, MobileNets, and ShuffleNets. Finally, we describe automated methods for designing efficient CNN architectures. Take-aways Describes the standard convolution layer and then describes three efficient variants of the standard convolution layer. Presents three representative manually designed efficient CNN architectures, including SqueezeNet, MobileNets, and ShuffleNets. Describes automated methods for designing efficient CNN architectures.
Pattern Identification of Bot Messages for Media Literacy

The massive use of online social media networks is a reality nowadays. Their increasing usage also raises growth in malicious activities in social media, one of which is the use of automated users (bots) that disseminate false information and can insert bias in analyses done on gathered social media data. Based on the concept of media literacy, this research presents a method to teach the human user to identify a pattern of a text produced by a bot, providing a tool (guide) to analyze social media text. Users who learned to identify a bot user with the guide had an average of 90% accuracy in the classification of new messages, against 57% of the participants who had no contact with the guide. The produced guide received a usefulness rating between 4 and 5 by the participants (scale from 1 to 5, with 5 being the highest value).
Augmented / virtual / mixed reality systems optical terminology

Abstract The AR / VR / MR systems field has recently expanded to many different applications and markets, and created a need for a common language to clear the misunderstanding and misinterpretation of terminology and to ensure efficient communication. We want to share our experience of more than two decades with the professional community and create a standard for everyone, starting from entrepreneurs with use case and application ideas, system engineers that define the system architecture and requirements, optical designers and engineers that develop these systems, assembly and testing engineers, and technicians.
Introduction to Computer Vision

This chapter will focus on what computer vision is, why we need it, the evolution of the technology, its different applications, and how it is used in Augmented Reality.
On the Legal Liability of Search Engine Data Mining Behavior Under Robots Protocol

In this paper, it is considered that robots protocol is a common practice in the industry. In judicial practice, we should give them the legal effect of commercial customary law and recognize the significance of copyright law. After the signing of the Convention on self regulation of Internet search engine services, robots agreement has been upgraded to industry self-discipline and standard in China, which should be generally observed. From the perspective of legal behavior, robots agreement is a promissory contract. The behavior of setting robots agreement on the website is to send out an offer, and the crawler of search engine makes an acceptance by grabbing behavior, and the agreement is reached and the contract is established and effective. However, robots agreement is a standard contract, and only when it is reasonably used can it be binding on both parties. Therefore, the data mining behaviors of search engines that violate or ignore robots protocol may result in concurrence legal liabilities such as breach of contract, infringement of information rights such as intellectual property rights, privacy infringement, competition legal liability, etc.
Nearly Tight Bounds for Discrete Search under Outlier Noise

Previous chapter Next chapter Full AccessProceedings Symposium on Simplicity in Algorithms (SOSA)Nearly Tight Bounds for Discrete Search under Outlier NoiseAnindya De, Sanjeev Khanna, Huan Li, and Hesam NikpeyAnindya De, Sanjeev Khanna, Huan Li, and Hesam Nikpeypp.161 - 173Chapter DOI:https://doi.org/10.1137/1.9781611977066.11PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Binary search is one of the most fundamental search routines, exploiting the hidden structure of the search space. In particular, it cuts down exponentially on the complexity of the search assuming that the search space is monotone. This paper is prompted by a basic question - how does the query complexity of the search problem change if the data has corruption? In particular, we study the powerful outlier noise model and assuming a bound on the fraction of such corruptions, establish nearly matching upper and lower bounds for the following problems: (i) binary search on an ordered set of size [n]; (ii) search on the posets {0, 1}d; and (iii) search on the posets [n]d. In all three cases, we use randomization to create robust versions of classical algorithms for these problems that handle corrupted data with relatively small performance penalties, specified as a function of the amount of corruption K. We complement these algorithmic results with almost matching lower bounds that show that no randomized algorithm can solve these problems with a smaller performance hit on the query complexity as a function of K. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-706-6 https://doi.org/10.1137/1.9781611977066Book Series Name:ProceedingsBook Code:SOSA22Book Pages:v + 320
Faculty Employment Conditions

Higher Education AbstractsVolume 56, Issue 4 p. 337-338 FACULTY EMPLOYMENT CONDITIONS Faculty Employment Conditions First published: 08 January 2022 https://doi.org/10.1111/hea.17_12178Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume56, Issue4October 2021Pages 337-338 RelatedInformation
Model-Based Approaches to Recommendation

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DiffXtract: Joint Discriminative Product Attribute-Value Extraction

Identifying discriminative attributes between prod-uct variations, e.g., the same wristwatch models but in different finishes, is crucial for improving e-commerce search engines and recommender systems. Despite the importance of these discrimi-native attributes, values for such attributes are often not available explicitly and instead are mentioned only in unstructured fields such as product title or product description. In this work, we introduce the novel task of discriminative attribute extraction which involves identifying the attributes that distinguish product variations, such as finish, and also, at the same time, extracting the values for these attributes from unstructured text. This task differs from the standard attribute value extraction task that has been well-studied in literature, as in our task we also need to identify the attribute, in addition to finding the value. We propose DiffXtract, a novel end-to-end, deep learning based approach that jointly identifies both the discriminative attribute and extracts its values from the product variations. The proposed approach is trained using a multitask objective and explicitly models the semantic representation of the discriminative attribute and uses it to extract the attribute values. We show that existing product attribute extraction approaches have several drawbacks, both theoretically and empirically. We also introduce a novel dataset based on a corpus of data previously crawled from a large number of e-commerce websites. In our empirical evaluation, we show that DiffXtract outperforms state-of-the-art deep learning-based and dictionary-based attribute extraction approaches by up to 8% F1 score when identifying attributes, and up to 10% F1 score when extracting attribute values.
Reliable Edge Intelligence Using JPEG Progressive

Edge intelligence (EI), the interdisciplinary field of edge computing and artificial intelligence (AI), aims at achieving time-critical AI services. Existing work mainly focuses on task offloading and model optimization to realize low latency as well as low power consumption. However, to guarantee low latency in lossy mobile network, user datagram protocol (UDP) is preferred to the transmission control protocol (TCP). Although image files can tolerate a small portion of corruption, latent packet loss and out-of-order can damage the overall service reliability. To improve it, we found that progressive encoding in joint photographic expert group (JPEG) standard tends to keep low-frequency coefficients in the first place compared to the commonly used baseline encoding. This paper studies whether JPEG Progressive achieves more reliable EI than JPEG Baseline in unreliable network environment. In our experiments, including scenarios of single packet loss, multiple packet loss and random packet loss, JPEG Progressive achieves significantly higher inference performance, with huge benefit in latency due to UDP.
The blockchain technology

Blockchain technology was invented to create Bitcoin. Introduced to the world in 2008, in a White Paper titled “Bitcoin: A Peer-to-Peer Electronic Cash System” published under the pseudonym Satoshi Nakamoto, whose true identity has never been revealed, bitcoin is defined as “a purely peer-to-peer version of electronic cash [that] would allow online payments to be sent directly from one party to another without going through a financial institution” (Nakamoto, 2008). The stated Goal of Bitcoin was to reduce transaction costs, fraud and payment uncertainties in digital transactions. It was proposed as an alternative to the prevalent model of e-commerce that relies on financial institutions serving as trusted third parties to process electronic payments.
Collaboration and advance planning across campus create more cybersecure colleges, universities

“Cybersecurity is a threat that changes all the time, so addressing it involves everybody living and breathing security as second nature.” This was a key theme of a National Student Clearinghouse webinar titled “CyberSecurED: Building a Cyber‐Ready Campus.” The webinar panelists — experts from EDUCAUSE, the Cyber Readiness Institute, REN‐ISAC, and NSC — discussed best practices for preventing and responding to cyberattacks.