Reviewerly/exHarmony huggingface dataset:
Consistent disjunctive sequent calculi and Scott domains

Abstract Based on the framework of disjunctive propositional logic, we first provide a syntactic representation for Scott domains. Precisely, we establish a category of consistent disjunctive sequent calculi with consequence relations, and show it is equivalent to that of Scott domains with Scott-continuous functions. Furthermore, we illustrate the approach to solving recursive domain equations by introducing some standard domain constructions, such as lifting and sums. The subsystems relation on consistent finitary disjunctive sequent calculi makes these domain constructions continuous. Solutions to recursive domain equations are given by constructing the least fixed point of a continuous function.
Multi-View Human Model Fitting Using Bone Orientation Constraint and Joints Triangulation

We address 3D human pose and shape estimations from multi-view images. We use the SMPL body model, and regress the model parameters that best fit the shape and pose. To solve for the parameters, we first compute 3Djoint positions from 2D joint estimations on images by using a linear algebraic triangulation. Then, we fit the 3D parametric body model to the 3Djoints while imposing a bone orientation constraint between the 3D model and the corresponding body parts detected in the images. We do so by minimizing a new set of objective functions through a two-step optimization process that provides a good initialization for the refinement of the shape and pose parameters. Our approach is evaluated on the Human3.6M and HumanEva benchmarks, showing superior results with respect to state-of-the-art methods.
Research on Energy-saving Campus Construction Based on Energy Consumption Monitoring Platform

Abstract At present, the hardware conditions of colleges and universities are getting better and better, but in contrast, the waste of resources is becoming more and more serious. In response to the call of environmental protection, universities are also actively seeking ways to reduce the waste of resources and build energy-saving universities. Because of staff and students’ daily teaching and living needs, colleges are looking for ways to save money. In this process, the energy consumption monitoring platform plays a great role. This paper is based on the energy consumption monitoring platform to study the construction of energy-saving campus.
Cryptospatial blockchain sharia hotel based on practical Byzantine fault tolerance

This research develops an application based on android that uses four interconnected nodes in a network with the Practical Byzantine fault tolerance (PBFT) blockchain method to value object geospatial retrieval. First, this application uses two types of users, general users and surveyors. Second, a surveyor location is validated within a radius of fifty meters from the central point of assets location using midpoint circle validation. Third, PBFT is used to access and validate and guarantee consistency by data using JSON-DIFF algorithm.
The Return of Medium Specificity Claims and the Evaluation of the Moving Image

This chapter reviews the traditional notion of medium specificity and its relevance to evaluation, along with recent revivals of the thesis in the work of Berys Gaut and Dominic Lopes. The chapter criticizes various versions of the medium specificity thesis – both past and present -- and offers an alternative approach to the evaluation of moving images.
Effect of AI Agent’s Speech and Tactility Types on Users’ Perception

Smart speakers have different speech style depending on the installed artificial intelligence (AI). Furthermore, the AI agent’s appearances can make different impressions. Hence, it might give characters to the AI agent through speaking and appearance modalities. To determine how an AI agent’s speech and tactility types affect users’ perception, we designed a 2(speech types: assistant-like vs. companion-like) × 2(flexibility types: flexible vs. hard) × 2(roughness types: rough vs. smooth) mixed-participant experiment (N=48). As a result, when the speech style is like a companion, it is possible to give an impression of sociability through a flexible material. However, there was no significant difference by flexibility type when being an assistant. In addition, there was a significant interaction effect between speech types and flexibility types on usefulness. When the speech type is companion-like, the AI agent with flexible material was perceived as being more useful and providing better services than that with hard material. On the contrary, when the speech type is assistant-like, the opposite result was revealed. Regardless of the speech type, a flexible material increases the impression of sociability with higher service evaluation, and a rough finish gives the AI agent an impression of usefulness with higher service evaluation.
Deep Interpretation with Sign Separated and Contribution Recognized Decomposition

Network interpretation in context of explainable AI continues to gather interest not only because of the need to explain algorithm decisions, but also because of potential improvements that can be made to network design. A large pool of research effects have been made including explanation by training sample representer points, exhaustive feature occlusion methods, locally learned interpretable models, sensitivity methods using network gradients, and relevance models using layer-wise back-propagation. It is however a constant challenge to interpret different network architectures or even different network function layers given the multiplicity of models, tools, rules and assumptions. In addition, there are challenges in producing good interpretable results; in particular, that of jointly improving both the sensitivity and relevancy of each attribute contribution within a network to the final network decision. A unified decomposition rule based on new propositions about negative features and majority contribution is proposed in this paper to address these challenges. Furthermore, quantitative measures are discussed to address performance of both sensitivity and relevancy of interpretation.
A Machine Reading Comprehension Framework for REST API Information Extraction

research-article A Machine Reading Comprehension Framework for REST API Information Extraction Share on Authors: Weixue Liu Beijing University of Posts and Telecommunications, China Beijing University of Posts and Telecommunications, ChinaView Profile , Lei Wang Beijing University of Posts and Telecommunications, China Beijing University of Posts and Telecommunications, ChinaView Profile Authors Info & Claims ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information SystemsMay 2021 Article No.: 92Pages 1–5https://doi.org/10.1145/3469213.3470294Online:18 August 2021Publication History 0citation23DownloadsMetricsTotal Citations0Total Downloads23Last 12 Months23Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Template Protected Authentication based on Location History and b-Bit MinHash

Various services ranging from finance to public services are digitalized in recent years for higher efficiency and user convenience. With this service digitalization, the need for identifying and authenticating users is increasing. Amongst the user authentication methods, biometric authentication is spreading as it does not require the user to remember a password or to have a specific token. As a more convenient authentication method, research is also being conducted on unconscious authentication using smartphones’ movement history. In this paper, we propose location history-based implicit user authentication acquired through GPS-equipped mobile devices. This method enables hands-free user authentication just by having a mobile device. However, location data are sensitive information that needs to be secured from the risk of location data leakage. By using the template protection technique, location data can be transformed so that the original location data cannot be recovered while enabling authentication. However, it has a trade-off between security and accuracy and remains as a problem to be solved. This paper proposes a new location history matching method based on Modified Weighted Jaccard Coefficient. Then it extends it to template protected location history authentication by presenting a new template protection technique using b-Bit MinHash. Our experimental results show that our proposed location matching method achieves practical accuracy compared with the conventional location history matching method. Furthermore, our template-protected location authentication has comparable accuracy to unprotected matching.
An Efficient FHE Radix-2 Addition Algorithm in BGV Scheme

Abstract Fully homomorphic encryption (FHE) is a novel encryption method that can perform operations on encrypted data. The performance of applications based on FHE is still low due to the high computational complexity of operations on the ciphertext. In this paper, we proposed an efficient FHE radix-2 addition algorithm in BGV scheme, called Optimized Cheon’s SIMD Adder. It is an optimization of Cheon’s SIMD Adder. The proposed adder uses the previously calculated results as much as possible to calculate the new result, so that it can keep the circuit depth as 0(log(μ)) and reduce the number of homomorphic multiplications. We also compared the weighted computational complexity (WCC) between this work and Cheon’s. The WCC of this work is only O (μ) , while Cheon’s is 0( μ· log (μ )). At the end of the paper, we implemented the proposed addition, which improves the calculation speedup by 1.3 ~ 1.4 times for 8 ~ 512- bit adders compared with Cheon’s SIMD Adder.
CSF: convolution sequential fusion model for session recommender system

Capturing users&#x2019; specific preferences is a fundamental problem in a large-scale recommender system. Currently, Sequence-based session recommendation has become a hot research direction in the industry. However, the current approach is somewhat inadequate in capturing user&#x2019;s long-term preferences. We propose a session model based on convolution sequential fusion (CSF) to capture users' specific preferences by fusing users' long-term behaviors and short-term sessions. Compared with existing sequential recommendations, we address two inherent weaknesses of session recommendations: 1) The interaction of long-term interest features is not sufficiently extracted. 2) The combination of long-term interest and short-term interest is too simplistic. The long-term interests of users are varied and complex. simultaneously, users' long-term interests are closely related to their short-term interests, so they should be better integrated. We propose to encode the behavior sequence with two corresponding components: the convolutional network for interactive extraction of users' long-term interests and the long-short term gated fusion module for better combination of long-short preferences Our entire model has been test on multiple real-world data sets, and the results demonstrate that our model is more effective than other recent models on multiple evaluation benchmarks.
Analytical Study on Algorithms for Content-Based Mobile Phone Recommendation System

Recommendation of anything helps in filtering out unwanted and irrelevant products from the entire work set which are not of any use or do not add any importance or value to the current task set to be accomplished. Mobile phone recommendation systems could be a probable revolution in the near future, as the telecommunication and mobile phone production industries are increasing exponentially in the market. Such systems would extract description of similar mobile phones which are most correlated with the mobile phone of user interest. The proposed system carries out this task taking the dataset which comprises of mobile phones and its features extracted from the e-commerce Web site ‘Flipkart’. The dataset comprises of 6917 records in total each one corresponding to a unique mobile phone in the Web site. The proposed model finds the similarities and does the predictions based on the input features in the dataset.
Detecting Phishing Websites Through Domain and Content Analysis

Many security incidents within the digital space focus on deceiving human users through the use of phishing websites that are built to capture and steal credentials. Phishing websites are designed to look similar to legitimate websites. In this paper the authors focus on developing a new solution for detecting specific phishing websites based on domain and content analysis. A phishing website is detected when it’s domain is similar to a legitimate service or includes the legitimate service name as a subdomain. Content analysis is performed to detect if the suspicious website contains elements of an authentication form used to steal credentials.
Big Data Technologies in DataBio

Abstract In this introductory chapter, we present the technological background needed for understanding the work in DataBio. We start with basic concepts of Big Data including the main characteristics volume, velocity and variety. Thereafter, we discuss data pipelines and the Big Data Value (BDV) Reference Model that is referred to repeatedly in the book. The layered reference model ranges from data acquisition from sensors up to visualization and user interaction. We then discuss the differences between open and closed data. These differences are important for farmers, foresters and fishermen to understand, when they are considering sharing their professional data. Data sharing is significantly easier, if the data management conforms to the FAIR principles. We end the chapter by describing our DataBio platform that is a software development platform. It is an environment in which a piece of software is developed and improved in an iterative process providing a toolset for services in agriculture, forestry and fishery. The DataBio assets are gathered on the DataBio Hub that links to content both on the DataBio website and to Docker software repositories on clouds.
Marmoset Monkeys Model Human Infant Gaze?

Macaque monkeys exhibit behavior similar to human adults while freely viewing video stimuli. However, the relationship to human infants has not been explored. Method: This paper compares published results from four datasets including novel analysis. We summarize reports comparing human infants to adults Franchak et al. [1], human adults to (adult) macaque monkeys Shepherd et al. [2], Berg et al. [3], and human adults versus macaque and marmoset monkey behavior Chen et al. [4]. Results: Bottom-up models of visual attention (“saliency map”) predict gaze at similar rates for all species and age-groups. However, when there are multiple salient targets, human adults and human infants over 12 months tend to look at the same target. In contrast, marmoset monkeys, like human infants under 9 months, look among many of the salient targets. Macaque behavior is in between, leaning towards human adult behavior. Conclusion: looking behavior of humans over 12 months and macaques is influenced by top-down control that selects among salient targets, whereas infant and marmoset behavior is not.
Study of using hybrid deep neural networks in character extraction from images containing text

Machine learning models are widely used in solving real world problems.Although they were introduced a long time ago, due to lack of computational power and availability of signifi cant amount of data, usage of these models was limited.Today however, due to the availability of high end machines and Cloud support, layer wise training has taken leaps in every domain.In the recent times, many researchers have found
Quantum Information Hardware Based on Color Center Nanophotonics

Color centers integrated into nanophotonic devices offer functionalities applied in quantum communication, simulation and computation. We explore various regimes of single-emitter and multi-emitter cavity quantum electrodynamics for applications in quantum information hardware.
Implementation of reward-based methodology in web blogging environment

Content Writing is something that everyone can look for as a medium to kick start their earning process. We have seen people earning a lot from their blogging career, but at the starting, we have seen people be skeptical to start with blogging. The main reason we can point out would be the absence of a roadmap and learning proper methods might take a long time to earn and they might lose hope. So, what we are trying to do is we are including a reward system into a blogging platform that will award people according to their writing's popularity. This system is quite similar to that we have seen on Twitch and YouTube. Twitch features a bits system and YouTube features a membership feature. This implementation would result in promoting more people to start their online business journey smoothly as content writing is something everyone needs in any type of online business.
Research on Intelligent Desk Design

To explore the innovative design of intelligent desk. With researching and analyzing the current situation of intelligent desk, this paper figures out problems and design trends of intelligent desk, and formulates the feasibility of realizing intelligent desk. The demand for intelligent desk is put forward, and the user demand is transformed into design language through demand transformation. The design direction of intelligent desk is defined by design conception logic, and the innovative design scheme of intelligent desk is determined. The scheme is described in detail and analyzed and evaluated. The design of intelligent desk based on the user’s demand and the characteristics of intelligent furniture is in line with the furniture design process and has certain feasibility and has certain reference significance for the design and production of intelligent furniture.
Chinese Word Embedding Learning with Limited Data

With the increasing demands of high-quality Chinese word embeddings for natural language processing, Chinese word embedding learning has attracted wide attention in recent years. Most of the existing research focused on capturing word semantics on large-scaled datasets. However, these methods are difficult to obtain effective word embeddings with limited data used for some specific fields. Observing the rich semantic information of Chinese fine-grained structures, we develop a model to fully fuse Chinese fine-grained structures as auxiliary information for word embedding learning. The proposed model views the word context information as a combination of word, character, pronunciation, and component. Besides, it adds the semantic relationship between pronunciations and components as a constraint to exploit auxiliary information comprehensively. Based on the decomposition of shifted positive pointwise mutual information matrix, our model could effectively generate Chinese word embeddings on small-scaled data. The results of word analogy, word similarity, and name entity recognition conducted on two public datasets show the effectiveness of our proposed model for capturing Chinese word semantics with limited data.
Opportunities and risks faced by the combination of blockchain and supply chain finance

After more than ten years of rapid development, blockchain technology has now entered the era of hyperledger 3.0. More and more industries have integrated blockchain technology and are constantly experimenting and exploring, especially the supply chain finance industry. Give full play to the role of blockchain technology in promoting supply chain finance, and study the challenges and challenges faced by the application of blockchain technology in the supply chain finance industry. This paper analyzes the content analysis of foreign internal scholars on the supply chain through qualitative research. The analysis of financial cases and research summarizes the challenges and challenges faced by the application of current blockchain technology in the supply chain finance industry. Through analysis, blockchain technology can be cut and supply chain financial services have good adaptability. Blockchain technology can play an important role in promoting smooth information flow, coordination of entities, risk control, and process simplification for supply chain financial services, but at the same time the application of blockchain technology still faces multiple challenges including laws and regulations, technical aspects, regulatory measures, risk prevention, and application efficiency. It is necessary for the country to advance legislative work and introduce technical standards as soon as possible to promote public chains, alliance chains and proprietary chains. The development and application of the chain in technology.
Computer Application of Game Map Path Finding Based on Fuzzy Logic Dynamic Hierarchical Ant Colony Algorithm

Abstract Ant colony algorithm has a wide range of application value in robot path planning. When the robot encounters obstacles, it can find the optimal path in the model space according to relevant performance indexes, and the collision times from the starting point to the target position are the least. In this paper, the research significance of ant colony algorithm in game map path finding is briefly discussed, and the application of ant colony algorithm in map path finding is explained for readers’ reference.
On the Use of Handheld Augmented Reality for Inventory Tasks: A Study with Magazine Retailers

In this paper we investigate if handheld augmented reality, in the form of an application running on a mainstream smartphone, can serve as a practical and effective tool for inventory tasks. Taking magazine retail as an example, we have applied a user-centered design process to research, design, implement and evaluate a handheld AR application prototype. We conducted a qualitative user study at magazine retail stores, where staff responsible for magazines were interviewed (\(n=8\)) and their primary magazine handling tasks observed. After an analysis of the study findings, we selected a key task as the basis for the design, implementation and test of an AR app prototype. The task consisted of collecting and registering a list of magazines for return to the distributor. We evaluated the AR app prototype in a user study (\(n=22\)), where participants used it to perform the selected task. They also performed the task using the paper list currently in use, and a second, simplified app prototype, without AR features. Task performance was measured based on time and error rate. The participant’s subjective experience was also captured in the form of a post-task survey and interview. Our findings suggest that handheld AR can prove effective when used for specific, focused tasks, rather than more open-ended ones.