Reviewerly/exHarmony huggingface dataset:
Could the Adoption of Quick Response (QR) Code in Lectures Enhance University Students’ Satisfaction? A Case Study of Hospitality and Tourism Programs in Macau

Ever since Quick Response (QR) Code was invented in 1994, it has been adopted in various industries with soaring popularity. In recent years, it was considered as a user-friendly and dynamic tool for education. Increasing numbers of teachers have employed QR code in lectures to stimulate interest, strengthen understanding and enhance satisfaction. This paper examines students’ satisfaction to QR code usage in university classroom lectures. University students of tourism program in Macau were invited to complete a self- administrated questionnaire. Among them, 162 valid samples are chosen. Students’ attitude towards QR code usage and their satisfaction level were measured. The results of students with different demographic characteristics were evaluated. The result provides insights for academia and teachers in higher education institutes for interactive and innovative teaching plan which would enhance student’s satisfaction.
The Design of Embedded Web System based on REST Architecture

Aiming at the problems of high coupling, large resource waste and low expansibility in traditional embedded WEB system development mode, this paper presents an embedded WEB system development design based on REST style. In this paper, data of the system is abstracted as a resource. The front-end initiates a resource request by the improved AJAX request, and calls the RESTful API to realize resource transmission, and the server processes the client requests through the CGI program. The implementation of this scheme improves the versatility and expansibility of embedded WEB system, and reduces the memory cost of server. It also provides a low-cost design method for embedded systems with limited storage resources.
Recurrent Neural Network Based Multi-Robot Route Planning for Steep-Land Harvesting Systems

Terrains with steep inclines cannot be utilized for crop production as it is hazardous to operate conventional farm equipment for various cropping tasks. This research, which is a part of a larger effort to deploy a team of robots for seeding and other similar tasks, proposes an approach for route planning of a team of ground mobile robots (drones) for seed delivery as well as an unmanned aerial vehicle (UAV) to continually replenish the drones with seeds. The overall cost is formulated in terms of linearly constrained integer quadratic programming (LCICQ). Using a Lagrangian function, a recurrent neural network with primal and dual sets of neurons is proposed to converge to optimal solutions.
Legal Ramifications of Blockchain Technology

The Blockchain technology deals with, and assures the security of assets and information over its network. However, if a breach occurs in some of its security features, and financial crimes such as fraud or tax evasion occurs, the law will have to be evoked to take its full course. Also, the Blockchain technology and the processes that accompany its implementation, exemplify a major deviation from the existing state of affairs in most financial activities, especially in the exchange of a legal tender for goods and services. Blockchain applications present contemporary methods of initiating and embarking on financial transactions in ways that do not conform to current legal structures. As such, having the judiciary decide on punitive measures for blockchain related financial crimes will require a meticulous assessment and revision of the rules guiding legal practice. It may be necessary for most countries to establish a nexus between certain scientific modules and ratification processes, so as to ensure that the modus operandi of blockchain applications agree with the law. This chapter seeks to provide a broad view of the relationship between blockchain and the Law, its effects on legal activities and how the Law can be used as a tool of protection in blockchain related transactions.
SecML: A Proposed Modeling Language for CyberSecurity

Cybersecurity is a comparatively new discipline, related to computer science, electrical engineering, and similar subjects. As a newer discipline it lacks some of the tools found in more established subject areas. As one example, many engineering disciplines have modeling languages specific for that engineering discipline. As two examples, software engineering utilizes Unified Modeling Language (UML) and systems engineering uses System Modeling Language (SysML). Cybersecurity engineering lacks such a generalized modeling language. Cybersecurity as a profession would be enhanced with a security specific modeling language. This paper describes such a modeling language. The model is described in sufficient detail to be actionable and applicable. However, suggestions for future work are also provided.
Inviolable Presence Registration of Mobile Entities in the ContextNet Middleware (Short Paper)

Several applications can benefit from recording information about the places a mobile entity visits and the length of time it spends there (e.g., shoppers, employees, buses, movable equipment, autonomous robots, etc.). In this paper, we present our approach to recording spatio-temporal information in a secure and inviolable way using a Distributed Ledger Technology. So far in this work, we observe that, although sending data to the blockchain is a heavy process, there are ways that this transaction time does not disrupt the flow of new data such as adding these data to a queue. However, this makes the sending process overall longer. We intend to continue this study by testing different blockchain services and comparing their performances.
An Improved Force-directed Algorithm Based on PageRank

Character relationship visualization is a very important research content in public opinion analysis. It makes full use of human visual perception ability and transmits a large amount of information in a limited space. The force-directed algorithm is the most classic layout algorithm for displaying character relationships. This paper proposes an improved force-directed algorithm based on PageRank to solve the problem that traditional force-directed algorithm can't show the importance of nodes. First, PageRank is used to compute the importance of the nodes. Then, the FR algorithm is used to map the nodes and relationships. Finally, drawing the chart based on visual factors. Experiments show that the algorithm is superior to the traditional force-directed algorithm in demonstrating the importance of characters.
An Image Recognition Algorithm Based on Self-Encoding and Convolutional Neural Network Fusion

With the advent of big data and the era of universal Internet, the powerful representation capability of convolutional neural networks in large-scale visual recognition is particularly important, but image recognition performance is limited by the influence of environmental noise. To this end, this paper proposes an image recognition algorithm based on self-encoding and convolutional neural network fusion. The noise reduction algorithm is used to reduce the noise of the input image, and then the trained convolutional neural network is used to identify the image. The noise reduction self-encoder not only makes the public training data set suitable for the actual environment, but also improves the usage rate of the training model, so that the model is decoupled from the specific environment. The experimental results show that the image recognition algorithm proposed in this paper can have a good recognition rate with/without ambient noise.
Metadata Driven Data Management in Distributed Computing Environments with Partial or Complete Lack of Trust between User Groups

The paper is devoted to the development of principles and operation algorithms of blockchain-based metadata driven data management system in distributed computing environments with partial or complete lack of trust between user groups belonging to different administrative domains. We also suggest a new method for delegation of rights from a user or service to another service within distributed computing systems. The implementation of the proposed approach, entitled ProvHL, is based on the permissioned blockchains and on the Hyperledger Fabric blockchain platform.
Crime Hidden in Email Spam

Email spam is one of the most overlooked crimes. Most people and law enforcement agencies dismiss it as a nuisance and do not deem it worthwhile to devote too many resources to reporting or investigating it. Very few researchers would see email spam as a research topic worth exploring, either. However, email spam can be and has become an ideal disguise for criminal activities. Aside from the fact that email spam itself is a crime, this article intends to introduce readers to the ideas regarding how spam may be linked to other crimes, including scam, fraud, illicit drug sales, electronic fencing, sex crimes, and malware. Moreover, email spam can be used to facilitate covert communication among criminals and provide guidance for entering the dark web without drawing too much attention from the authorities.
Usability of an Immersive Virtual Playground: Enjoyment, Authenticity, Effort and Cybersickness

Immersive virtual reality systems have the potential to provide users with an engaging and ecologically valid environment in which to practice motor and cognitive skills. The purpose of this study was to evaluate the usability of a virtual playground for three age groups (children, young adults, and older adults) who performed three tasks within an immersive virtual playground. All participants had an overall positive experiences and minimal cybersickness while playing in the virtual playground although there were some key differences between them.
Privacy-Aware Job Submission in the Cloud

The services offered by cloud computing are provided to individuals and organizations by varied shared resources which are forming the hardware layer of cloud data centers. Cloud users do not deal or interact directly with those resources, instead, they deal with the virtualized version of them, in other words, users deal with the virtualization layer which conceals to a great extent the specifics of the physical hardware layer. Based on the virtualization concept, more than one virtual machine can be co-hosted on the same physical machine. In spite of the wide range of benefits, co-hosting virtual machines on the same host comes with privacy and security threats. From one side, cloud providers are serving the virtual machines without being aware of their contents. On the other side, once cloud users submit their jobs to be serviced in the cloud, they lose their control on their jobs' sensitive information. Thus, cloud users' hesitation from moving to the cloud is logical since their sensitive jobs' content leakage or misuse is possible, especially when cloud services are not designed with privacy considerations. This paper proposes an approach to make the jobs with sensitive information more secure when submitted to the cloud environment. The core idea of the approach is to request the inclusion of the privacy specification of a set of one or more provider services in the Service Level Agreement contract.
A Type Theory for Probabilistic $$\lambda $$–calculus

We present a theory of types where formulas may contain a choice constructor. This constructor allows for the selection of a particular type among a finite set of options, each corresponding to a given probabilistic term. We show that this theory induces a type assignment system for the probabilistic $$\lambda $$–calculus introduced in an earlier work by Chris Hankin, Herbert Wiklicky and the author, where probabilistic terms represent probability distributions on classical terms of the simply typed $$\lambda $$–calculus. We prove the soundness of the type assignment with respect to a probabilistic term reduction and a normalization property of the latter.
Analysis of Matric Product Matching Between Cosine Similarity with Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec in PT. Pricebook Digital Indonesia

Consumers today have the option to purchase products from thousands of e-commerce. However, the completeness of the product specifications and taxonomies used to organize products differently in different electronic shop differently. To improve the consumer experience, Pricebook approach for integration of the product through the website to find the cheapest price from various platforms. In our writing, we do approach by using a model of neural language such as TF-IDF (term frequency-inverse document frequency) as well as Word2vec by using the method of cosine similarity. TF-IDF is a way to give the relationship a word weighting (term) against the document. Semantic vector or word embedding is one way to represent the structure of a sentence will be in align with manipulating sentences into vector shapes with Word2Vec. Cosine similarity method is a method to calculate the similarity between two objects that is expressed in two vectors by using keywords (keywords) of a document as the size so that it leads to more products matching good performance and categorization. In addition, we compare the results of the representation of the TF-IDF with Word2vec against a number of the data.
Increase Activity Time Efficiency in Official Documents Management using Blockchain-based Distributed Data Storage

In general, the electronic official documents system is designed with a centralized system and managed by each government agency, so that data and records of official documents process activities are stored in a centralized database and managed by a single organizational unit. However, there are some disadvantages of this centralized system, especially in the workflow of official documents that involve more than one organizational unit. i.e. dependence on third parties which is the sole authority of the official documents data management. If there is a system failure there is a certain period of time, it will increase the activity cycle time and decrease activity time efficiency in the official documents business process. The blockchain-based solution described in this paper is to avoid the occurrence of delay times during the activity cycle time in business processes when a system failure occurs which can cause a decrease in activity time efficiency. Blockchain makes the official documents system become “Trusless Operation” with each participant of different organizational unit having a copy of transaction data through a consensus mechanism. The simulation results show that our system can reduce time delay in activity cycle time during a system failure as long as the number of faulty peers within the tolerance limit so that it can increase the activity time efficiency.
Weighted average price management of manufacturer sales with determined realization volume by the end of trade period on commodity exchanges

This paper proposed a method to control the weighted average price of manufacturer sales with the initially specified sales by the end of the trading interval on the commodity exchanges. The developed manufacturer sales strategy is proposed as a hedge against price slumps without the use of derivative financial instruments in commodity markets.
Multi-3D-Object Tracking by Fusing RGB and 3D-LiDAR Data

Multiple object tracking (MOT) is a fundamental problem in the autonomous driving research community. Through accurate and efficient tracking, ego-vehicle can get the location velocity of surrounding objects and make a reasonable future motion planning. Different from most of the methods adopting the RGB or 3D-LiDAR data independently, this paper aims to track the perceived objects by fusing RGB and 3D-LiDAR data, the standard sensor configuration in current autonomous vehicles. Specifically, we firstly use Hungarian algorithm as a backbone model to associate the 3D point cloud of each object in adjacent frames. Then, we fully explore the appearance feature in RGB frame and geometrical feature in 3D point cloud to restrict the wrongly associate target IDs because of the interaction of near objects. We evaluate our method on the newly proposed BLVD dataset, and show the favorable performance.
NFGS Enabled Face Re-identification for Efficient Surveillance in Low Quality Video

People tracking, and re-identification of human faces have been widely applied in large areas namely, surveillance and human-robot interaction. The accuracy of the face reidentification largely depends on the quality of the image likewise the face recognition systems. In this piece of work, we have designed an algorithm to extract the important facial features from a low- face image by utilizing the method of figure-ground segregation inspired from neuro-visual structure in human visual cortex (NFGS). The aforementioned method describes the role of glial cells and formation of Extended classical receptive field (ECRF) further. This work illustrates improved face reidentification, tracking and recognition in low quality images including low resolution or small images, low contrast images, non-uniformly illuminated images and blurred images affected through motion. This in turn helps to perform re-identification and person tracking in offline videos. Face re-identification and face matching has been addressed in low quality image space through the proposed framework effectively as supported by the experimental evidences.
Communication of Hazards in Mixed-Reality Telerobotic Systems: The Usage of Naturalistic Avoidance Cues in Driving Tasks

This study investigates the effect of naturalistic visual cues on human avoidance behavior for a potential use in telerobotic user interfaces incorporating mixed-reality environments (e.g., augmented reality).Telerobotic systems used in hazardous environments require interfaces that draw operators' attention to potential dangers. Existing means of hazard notification can often distract or induce stress in operators. In the design and implementation of such interfaces, visual semiotics plays a critical role in creating more effective interfaces. Naturalistic visual cues such as Aposematism or Kindchenschema have proven effective to communicate danger or caution in nature, but the application of these cues in visual systems have yet to be thoroughly investigated.A study was conducted where 40 volunteering participants were asked to control a remote vehicle in a simulated environment. The environment contained a set of neutral and visually augmented obstacles that were designed to provoke avoidance behavior.The use of visual cues triggered greater avoidance behaviors in participants compared to neutral obstacles. The distance of avoidance was correlated with the type of cue present, with obstacles augmented by Aposematism (Cue A) having a greater participant-obstacle distance than Kindchenschema (Cue K).This study shows the potential for the incorporation of naturalistic visual cues as a means to designate warning or caution in telerobotic environments.The findings can offer practical guidelines for the design of visual cues in telerobotic interfaces. The further incorporation of such cues may reduce operator stress and the amount of human errors in telerobotic operations.
Transnational Cybercrime

Organized crime groups are involved in all kinds of transnational crimes. Lawbreakers can victimize people in other countries through international scams and cyber theft of financial information. Moreover, much of the harm from transnational crimes stems from activities of formal criminal organizations or criminal networks that connect individuals and organizations who undertake specific criminal acts together. According to the United Nations, annual proceeds from transnationally organized crime activities amount to more than $870 billion dollars with drug trafficking producing the largest individual segment of that total amount. One of the most difficult forms of transnational crime to combat is cybercrime. This article will review transnational cybercrime and it's technology.
Identification using the K -Means Clustering and Gray Level Co-occurance Matrix (GLCM) At Maturity Fruit Oil Head

This study discusses the identification of its palm fruit camp picture image using K-Means Clustering and identification GLCM. Process fruits traditionally experienced constrained due to human nature that has flaws that the desired results are not effective. Advances in computer technology have come into the world in terms of the farm before harvest and post-harvest. This Dimolished how to recognize the fruit so that it correspond to real conditions. Condition of oil palm fruit is determined by the level of maturity in terms of color, texture and shape of the oil palm fruit. Identification which did classify in the category of mature and not mature. Determination of identification with the K-means clustering method that uses the difference in euclidean distance and GLCM feature extraction as a reference. For the results of the present study is equal to 90% of the 50 test data.
Inciting Incidents: How Can We Motivate Family Conversations about Health?

In many families, there are obstacles to support and collaborate with one another around positive health outcomes. One obstacle in providing support to one another occurs when family members are unaware of the need. In this study, we examine aspects of family conversations about health that affect family members’ decision to share (or not to share) information within the family, specifically information about sleep behaviors and medication intake. We conducted an interview study with independent living elderly parents (n = 11) and adult children (n = 14). We present factors that motivate and discourage family members from talking about sleep and medication within the family. We identified that some family members shift sharing behaviors following life changes that we characterize as “inciting incidents.” We elaborate the concept of inciting incidents as a resource for design ideas and contribute with a synthesis of design insights for developing family-centered health technologies.
Top-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e577" altimg="si635.svg"><mml:mi>k</mml:mi></mml:math> user-specified preferred answers in massive graph databases

There are numerous applications where users wish to identify subsets of vertices in a social network or graph database that are of interest to them. They may specify sets of patterns and vertex properties, and each of these confers a score to a subgraph. The users want to find the subgraphs with top-k highest scores. Examples in the real world where such subgraphs involve custom scoring methods include: techniques to identify sets of coordinated influence bots on Twitter, methods to identify suspicious subgraphs of nodes involved in nuclear proliferation networks, and sets of sockpuppet accounts seeking to illicitly influence star ratings on e-commerce platforms. All of these types of applications have numerous custom scoring methods. This motivates the concept of Scoring Queries presented in this paper — unlike past work, an important aspect of scoring queries is that the users get to choose the scoring mechanism, not the system. We present the Advanced top-k (ATK) algorithm and show that it intelligently leverages graph indexes from the past but also presents novel pruning opportunities. We present an implementation of ATK showing that it beats out a baseline algorithm that builds on advanced subgraph matching methods with multiple graph database backends including Jena and GraphDB. We show that ATK scales well on real world graph databases from YouTube, Flickr, IMDb, and CiteSeerX.