Reviewerly/exHarmony huggingface dataset:
An Improved Merchant Recommendation Algorithm Based on User Reviews

The evaluation data in merchant review websites implies merchant attributes and user emotions. The existing personalized merchant recommendation methods do not achieve the desired efficiency in implicit recommendation, which ignores the fine-grained user preferences and the sentiment scores at the attribute level of merchants and users. Therefore, an improved merchant recommendation algorithm based on user reviews TWMR(Timing factors and user Weights Merchant Recommendation algorithm) is proposed in this paper. Firstly, extract and classify the attributes of the merchant, calculate the sentiment scores of the merchant attribute matrix and the user preference attribute matrix. Secondly, add time-series factors and user opinion weighting factors to the attribute matrix, and use similarity to match the user attribute matrix with the merchant attribute matrix. Finally, it is verified that the algorithm TWMR effectively improves the stability of the implicit recommendation effect by the experimental comparison on the Yelp data set, and a better effect on matrix matching recommendation is made.
Taking the Next Step Towards Convergence of Design and HCI: Theories, Principles, Methods

The current digital transformation of the world of things with more and more hybrid, interconnected artifacts leads to a convergence of the different disciplines involved in this development. A close transdisciplinary collaboration of design, computer science, environmental and cognitive psychology requires an integration of the disciplines’ knowledge bases. To this end, the article highlights selected theories, principles, and methods of product design and human-computer interaction, their parallel yet time-delayed development, and the existing overlaps. Among other things, it shows that the semiotics established in product design has so far only been superficially received in HCI. An integration of semiotic or semantic theory and a human-centered design approach is proposed to address current challenges of digital transformation and hybrid artifacts: such as the invisibility dilemma and the control dilemma posed by AI, as well as issues of sustainability inherent in the hybrid artefacts of the Internet of Things.
Virtual Exhibit Design: The UX of Student BFA Design Shows in Social VR

Creating exhibitions in virtual reality offers unique user experience design challenges. This paper examines a case study of a past VR exhibit and discussions on future UX and usability research for VR exhibitions. Due to COVID19, student BFA shows across the country were canceled as most Universities went virtual. In an effort to create virtual student shows, many programs put together websites or zoom-style showcases. At Fresno State University, the Graphic Design BFA students put on a virtual show using Social Web VR. This paper examines the UX design of the VR show. Next, it examines the UX planning for the 2021 BFA show using social web-based VR. Finally, the paper concludes with plans for researching best practices for the UX of Virtual Exhibition Design moving forward. While social web-based VR offers great potential, designers must be careful to keep VR Exhibits human-centered and consider all participants' total user experience.
Investigations on optimizing performance of the distributed computing in heterogeneous environment using machine learning technique for large scale data set

Enforcing admired machine learning approaches to huge data enhanced novel issues for researchers. Conventional libraries could not suitably fulfil the requirement of complex model with wide variety of data and system parameters. Therefore new methodologies are required performing the computation on more than one machine over distributed environment. Some distributed frameworks on huge data such as MapReduce and TensorFlow have been deployed to solve various machine learning problems in heterogeneous distributed environment. The objective of this paper is providing a wide variety of useful information about platforms, approaches, problems, datasets, and optimization approaches in distributed systems. So researchers have been used the beneficial information to develop new approaches for efficient machine learning. This paper also covers the various formats of data like structured, semi structured and unstructured big data. A brief review of previous works is also represented in text and tabular format to provide motivation to the researchers for developing new paradigm of distributed computing environment.
An Information and Predictive Credibility Control System Using Blockchain Technique

Abstract Since then, the use of the blockchains for emerging technologies such as AI has been explored by leading community science, where several actors or agents work together to settle for consensus. To do this, data must be converted into blockchain information in blockchain storage. This blockchains are referred to as blockchains based on information. Information blockchains can be useful for effective implementation of risk management. A previous work implemented the blockchain probabilistic which renders blockchains dependent on information simpler. This article suggests an extension of the definition of the stochastic blockchain. It is suggested that a social marketing system should be developed for such blockchains. It is planned to satisfy the needs of a wide variety of applications. In general, we use it to detect relevant nodes and reduce its effect on the consensus mechanism of the stochastic blockchains. We test the system using multiple adversary tactics by applying it to a benchmark. We also evaluate unilateral decisions both with and without identification of malicious nodes. Both outcomes indicate a sustainable efficiency, in which the planned work outcomes and results are outstanding.
Unbounded Fuzzy Radial Basis Function Neural Network Classifier

Shetty, Balaji S. Mahindrakar, Manisha S. Kulkarni, U. V.Area of pattern recognition deals with the recognition of patterns by using different machine learning algorithms without human intervention. Many different data mining algorithms are used in pattern recognition. The selection of an appropriate and precise algorithm is very crucial. An imprecise algorithm may lead to generate a wrong decision. Recognition can be supervised or unsupervised. This paper presents a novel unbounded fuzzy radial basis function neural network (UFRBFNN) classifier model to perform the supervised classification. This classifier is constructed using fuzzy clustering and further clusters are converted into fuzzy hyperboxes. Fuzzy set hyperboxes (FHBs) represent the neurons in the hidden layer. The creation of these FHBs is based on the unbounded spread from inter-class information and intra-class fuzzy membership function. The proposed approach is faster and independent of the tuning parameters. The output is determined by the union operation of the FHBs outputs which are connected to the class nodes in the output layer. Using K-fold cross-validation, the UFRBFNN model is verified by applying 7 different standard datasets from (UCI) machine learning repository and further by comparing results with well-known radial basis function neural network (RBFNN) variants. The analysis of the result shows that the proposed model provides 5–10% improved training accuracy with previous radial basis function classifiers.
Research and Design of Blockchain System for Logistic Tracing

The blockchain is a decentralized network, which can prevent information to be tampered and improves the security and transparency of data. Traditional centralized database to store logistics data is vulnerable to attacks and limits sharing of information. Information isolation and unreliable data make it hard to trace the process of logistics delivery. In this paper, we propose a blockchain based system to improve the performance of logistics tracing service. We design the overall structure of the blockchain system and build a prototype using Hyperledger Fabric. Smart contract is implemented to store the logistic records into the blockchain ledgers and create tracing lists for logistic orders. Performance evaluation shows that the system achieves the logistics tracing ability with good performance. This paper assumes that the IoT devices used in the logistics process are reliable and will not be attacked. In the further work, a smart contract-based access control model can be developed to overcome the vulnerability caused by IoT devices.
Short Text Entity Disambiguation Algorithm Based on Multi-Word Vector Ensemble

With the rapid development of network media, the short text has become the main cover of information dissemination by quickly disseminating relevant entity information.However, the lack of context in the short text can easily lead to ambiguity, which will greatly reduce the efficiency of obtaining information and seriously affect the user's experience, especially in the financial field.This paper proposed an entity disambiguation algorithm based on multiword vector ensemble and decision to eliminate the ambiguity of entities and purify text information in information processing.First of all, we integrate a variety of unsupervised pre-trained word vector models as vector embeddings according to different word vector models' characteristics.Moreover, we use the classic architecture of long short-term memory (LSTM) combined with the convolutional neural network (CNN) to fine-tune pre-trained Chinese word vectors such as BERT to integrate the output of entity recognition results.Then build the knowledge base and introduce the focal loss function on the basis of CNN and binary classification to improve the effect of entity disambiguation.Experimental results show that the algorithm performs better than the traditional entity disambiguation algorithm based on the single word vector.This method can accurately locate the entity to be disambiguated and has a good performance in disambiguation accuracy.
Marks of Ownership of Russian Far Eastern Book Collectors in the Late 19th — Early 20th Centuries

The Russian pre-revolutionary book features many various provenance marks including marks of ownership. Such marks indicate that a particular item belongs to a particular owner. They can have forms of supralibros (or super ex-libris), ex-libris (or bookplates), book stamps, signatures and inscriptions. There are also “non-specific” ownership stamps and labels, which do not have indications of their book nature (such as phrases “from the books/library of...”, “ex-libris…”) and can be used on any objects. Usually these are word rubber stamps (rarely — labels) with a text consisting of a book owner’s name and occasionally some additional information (such as an owner’s address, title, etc.). In Russia, bibliophiles and bibliologists have been studying bookplates for about two centuries. However, there are still many blank spots in the history of the Russian bookplate. Regional book ownership marks are mostly unexplored. This article presents an attempt to describe and analyse book ownership marks of the Russian Far Eastern book owners. Studying of library holdings of 13 regional institutions and conducting bibliographic research allowed revealing 58 local marks of ownership (excluding signatures and inscriptions) that belonged to 23 local book collectors of the pre-revolutionary period. The study showed that they used all types of marks from supralibros to inscriptions, but mostly preferred the “non-specific” book stamps and super ex-libris. There was also established that the bookplate labels were particularly rare — the author discovered the only specimen in the Far Eastern State Research Library. The predominance of book stamps as marks of ownership can be explained by their multipurposeness, cheapness and easiness of manufacturing. Far Eastern book owners frequently used two or three different types of marks together on one book, for example, book stamp and supralibros, inscription and book stamp or supralibros and inscription, etc. Often one collector used several different stamps or supralibros: almost 40% of book collectors had two or three marks of ownership. At the same time, 11 of 23 book owners additionally marked their books with the inscriptions.
Receding Horizon Control Based Motion Planning with Partially Infeasible LTL Constrains

This work considers online optimal motion planning of an autonomous agent subject to linear temporal logic (LTL) constraints. Since user-specified tasks may not be fully realized (i.e., partially infeasible) in a complex environment, this work considers hard and soft LTL constraints, where hard constraints enforce safety requirements (e.g. avoid obstacles) while soft constraints represent tasks that can be relaxed to not strictly follow user specifications. The motion planning of the agent is to generate trajectories, in decreasing order of priority, to 1) guarantee the satisfaction of safety constraints; 2) mostly fulfill soft constraints (i.e., minimize the violation cost if desired tasks are partially infeasible); 3) locally optimize rewards collection over a finite horizon. To achieve these objectives, receding horizon control is synthesized with an LTL formula to maximize the accumulated utilities over a finite horizon, while ensuring that safety constraints are fully satisfied and soft constraints are mostly satisfied. Simulation and experiment results are provided to demonstrate the effectiveness of the developed motion strategy.
Natural Interaction with Traffic Control Cameras Through Multimodal Interfaces

Human-Computer Interfaces have always played a fundamental role in usability and commands' interpretability of the modern software systems. With the explosion of the Artificial Intelligence concept, such interfaces have begun to fill the gap between the user and the system itself, further evolving in Adaptive User Interfaces (AUI). Meta Interfaces are a further step towards the user, and they aim at supporting the human activities in an ambient interactive space; in such a way, the user can control the surrounding space and interact with it. This work aims at proposing a meta user interface that exploits the Put That There paradigm to enable the user to fast interaction by employing natural language and gestures. The application scenario is a video surveillance control room, in which the speed of actions and reactions is fundamental for urban safety and driver and pedestrian security. The interaction is oriented towards three environments: the first is the control room itself, in which the operator can organize the views of the monitors related to the cameras on site by vocal commands and gestures, as well as conveying the audio on the headset or in the speakers of the room. The second one is related to the control of the video, in order to go back and forth to a particular scene showing specific events, or zoom in/out a particular camera; the third allows the operator to send rescue vehicle in a particular street, in case of need. The gestures data are acquired through a Microsoft Kinect 2 which captures pointing and gestures allowing the user to interact multimodally thus increasing the naturalness of the interaction; the related module maps the movement information to a particular instruction, also supported by vocal commands which enable its execution. Vocal commands are mapped by means of the LUIS (Language Understanding) framework by Microsoft, which helps to yield a fast deploy of the application; furthermore, LUIS guarantees the possibility to extend the dominion related command list so as to constantly improve and update the model. A testbed procedure investigates both the system usability and multimodal recognition performances. Multimodal sentence error rate (intended as the number of incorrectly recognized utterances even for a single item) is around 15%, given by the combination of possible failures both in the ASR and gesture recognition model. However, intent classification performances present, on average across different users, accuracy ranging around 89–92% thus indicating that most of the errors in multimodal sentences lie on the slot filling task. Usability has been evaluated through task completion paradigm (including interaction duration and activity on affordances counts per task), learning curve measurements, a posteriori questionnaires.
Image data security using Quasigroup combined with Fibonacci Q-transformation

This paper proposes an image encryption scheme using Quasigroup and Fibonacci Q-transformation. The proposed scheme is based on substitution–permutation structure, which can handle the images of any size, i.e., square as well as non-square. The Quasigroup of order 256 and Fibonacci Q-transformation are used as primitives for substitution and diffusion of image pixels, respectively. The encryption method is designed to have the secret keys applied with specific orders such that correct decryption is only possible when correct keys and correct orders are supplied together. The proposed scheme has good efficiency, an extensively huge key space and ability to resist all common attacks. The feasibility and performance of the proposed scheme is examined by conducting various experimental results. The robustness of the proposed scheme is validated by analyzing it against statistical and cryptanalytical attacks. Further, superiority of the proposed scheme is confirmed by comparing with the related works.
Risk-Sensitive Rendezvous Algorithm for Heterogeneous Agents in Urban Environments

Demand for fast and inexpensive parcel deliveries in urban environments has risen considerably in recent years. A framework is envisioned to enforce efficient last-mile delivery in urban environments by leveraging a network of ride-sharing vehicles, where Unmanned Aerial Systems (UASs) drop packages on said vehicles, which then cover the majority of the distance before final aerial delivery. This approach presents many engineering challenges, including the safe rendezvous of both agents: the UAS and the human-operated ground vehicle. This paper introduces a framework to minimize the risk of failure while allowing for the controlled agent's optimal usage. We discuss the downfalls of traditional approaches and formulate a fast, compact planner to drive a UAS to a passive ground vehicle with inexact behavior. To account for uncertainty, we learn driver behavior while leveraging historical data, and a Model Predictive Controller minimizes a risk-enabled cost function. The resulting algorithm is shown to be fast and implementable in real-time in qualitative scenarios.
A New Environment: Augmented Reality

In brief, augmented reality can be defined as a new “environment”, which is created by simultaneously transferring virtual data such as sound, image, graphic produced in virtual environment to real environment.It is seen that the augmented reality has great potential in idea generation, development, presentation to users and information transfer as a representation (presentation) tool with its current applications. Accordingly, the purpose of this study is to evaluate the use of AR technology in cultural heritage areas, to study application examples in archaeological sites and to examine the potential contributions that different uses can provide. Within the framework of this research, AR application is planned to be developed for the ancient city of Alexandria Troas, one of the important cities of the Troas region, located in Çanakkale. With the application created, it is aimed to understand the value of the historical artifacts and structures unearthed during the excavation, to transfer them to the people who will experience the area, and to keep the city alive within the framework of virtual heritage values.
Health Data Management for Nursing Practice: An Intelligent, Holographic Mixed-Reality System

The elderly population in need of long-term care is rising, adding to the workload of healthcare workers in nursing homes. Throughout a work day health workers often must perform the time consuming process of collecting various types of health data from the residents. A recent study has revealed that frontline nurses and caregivers spend over half of their time accessing and updating patient records instead of providing care. New technology could enhance these data-oriented operations, and free up time so that staff can care for patients in other ways. Intelligent human-computer interaction paradigms can enhance data retrieval, visualization, and communication processes efficiently in a way to improve the overall productivity during work. In this study, we present an intelligent holographic mixed reality (MR) system that leverages Microsoft Hololens for work practices of healthcare work in nursing homes. Supported by computer vision, this system provides automatic data retrieval, in addition to interactive data visualization and mid-air data entry mechanism. Using face recognition through the Hololens’ visual sensors, the system recognizes individual patients, and automatically retrieves relevant health data and supports data entry.
Research on Military Text Machine Translation Based on Deep Neural Network

This paper analyzes the difficulties of current military text machine translation and proposes a multi-strategy fusion translation method. This method can automatically align and manually proofread the user's historical data, develop bilingual training data in the field, integrate the user's accumulated domain terminological dictionary into the neural network machine translation process, and provide a variety of incremental learning methods to make use of the new data. It effectively improves the engine's translation ability of military texts, and solves the problems of inaccuracy and inconsistency in terminological translation, keeping improving the translation performance of the system in the process of application.
Optimized Neural Network for Big Data Classification Using MapReduce Approach

This paper proposes a big data classification approach over big data based on the MapReduce framework. In the mapper phase, feature selection is carried out by selecting features based on Principal Component Analysis (PCA). Once feature selection is made, the selected features are passed to the reducer phase, where classification is done by the proposed Rider Neural Network (RideNN) categorizes the data into two classes, like normal and abnormal classes. The proposed RideNN classifier achieves a high accuracy of 0.932, maximal sensitivity of 0.831, and maximal specificity of 0.958 based on the Cleveland dataset.
Secure B-IoT Based Smart Agriculture– A Brief Review

Abstract The future world is moving towards creating innovative technology where intelligent Smart lifestyles are created in homes, smart farming in the agriculture sector, and smart cities. The IoT is one of the world’s most famous inventions in the decade. Life is made comfortable by IoT by connecting devices and generating more than billions of data. However, a major concern in IoT is data security as it handles a large number of complex data. A security issue in IoT devices is a major concern. Blockchain IoT is a possible protection option. Blockchain IoT technology is used for enhancing protection in agriculture. The key focus is to resolve various phases of the farm supply chain by developing procedures. This paper discusses the security issues in IoT and how it overcomes by integrating IoT with Blockchain. Further, the discussion involves the application of IoT blockchain security in Precision farming.