Reviewerly/exHarmony huggingface dataset:
Pre-training local and non-local geographical influences with contrastive learning

Geographical influences fundamentally help to improve the performance of location-based services (LBS). However, existing LBS approaches rely on abundant task-specific labeled data in an end-to-end manner, which often causes overfitting and sparsity problems. One effective way is to pre-train contextualized representations on unlabeled data with self-supervision to capture intrinsic correlations between locations and their contexts. In this paper, we propose a novel local and non-local geographic representation (LNGR) model with contrastive self-supervised learning, which is able to simultaneously incorporate geospatial proximity as a local geographical influence and relative distance differences as a non-local geographical influence. To capture the inherent dependency between the geographical influences, we pre-train sequential (for non-local) and surrounding (for local) contextual encoders in a unified framework with three different types of self-supervised objectives, hence promoting the quality of contextual point-of-interest (POI) representations. We evaluate our pre-trained model for next POI recommendation on six check-in datasets. The extensive experimental results demonstrate that the superiority of LNGR over existing pre-training and end-to-end recommendation methods. Besides, we further show the effective robustness and generalization ability of our pre-trained model when task-specific labeled data is scarce.
A flexible scope theory of intensionality

Abstract Extant attempts to incorporate intensionality into the grammar either systematically over-generate, or systematically under-generate. In this paper, building on Keshet (Linguist and Philos 33(4):251–283, 2011), we aim to reconcile a scopal account of de re with the possibility of de re readings out of scope islands. By adapting compositional techniques for dealing with exceptionally scoping indefinites (Charlow, in On the semantics of exceptional scope, PhD thesis, Rutgers University, 2014; The scope of alternatives: indefiniteness and islands. Linguist and Philos 43(4):427–472, 2020), we develop an intensional grammar in which exceptional de re is achieved via cyclic scope . World-sensitive expressions are converted into scope-takers via a constrained inventory of type-flexible operators. Type flexibility explains the possibility of apparently island-violating de re by predicting the possibility of cyclic scope-taking. We argue that the resulting theory—which we dub the flexible scope theory —is sufficiently expressive to address the under-generation issues of current accounts, while still capturing constraints on de re in an explanatory fashion.
Ontology Integration by Semantic Mapping for Solving the Heterogeneity Problem

In recent years, ontology integration has received an increased focus in ontology engineering. Ontology integration is a complex process that has some difficulties such as semantic heterogeneity. The goal of this research is to use semantic mapping to reduce integration complexity and solve semantic heterogeneity. What is ontology engineering? What difficulties haven't been solved until now by ontology integration? What is the effective role of semantic mapping in semantic heterogeneity? This research seeks to address these questions. The expected contribution of this research is to build a comprehensive view of ontology integration and support interoperability. The significance of using semantic mapping to improve interoperability on ontology integration is confirmed by researchers.
Comparative Analysis of USB and Network Based Password Cracking Tools

Passwords play a major role in securing various applications. It is used for authentication in Operating system login, web services, ATM machines and many more. Even though we educate the end users to choose a complex password, there are several attacks that can break through such password. With passwords also being a very attractive asset for the attackers to have their hands on, the methods of attacks are many. In this paper, we provide a comparative analysis of USB based and Network based attacks on passwords. We provide detailed description and comparison of various network based and USB based password cracking tools. For the comparison, we benchmarked the passwords into Easy password, Hard password, and Medium Password. We then measured the time required to crack these passwords using various tools.
Research on the Application of Virtual Reality Technology in Physical Education in Colleges and Universities

The rapid development of the global information industry and the iterative update of cutting-edge high-tech technologies are gradually leading countries around the world to an intelligent development path based on 5G technology, artificial intelligence and other high-tech technologies. With the widespread intelligentization of terminals in the education system, virtual reality technology is gradually being widely used in the field of education and teaching. Physical education in colleges and universities is a highly specialized subject, which requires teachers to demonstrate in person in the process of imparting knowledge, and puts forward very high requirements for teachers' technical teaching. Based on the technical difficulties in college sports technology courses and the actual demand for auxiliary teaching tools, this article uses the method of literature materials, questionnaires, expert interviews, mathematical statistics and other methods to carry out a study on 7 colleges and universities with strong sports majors in Shandong, China. The research and analysis aims to explore the auxiliary teaching of sports technology through virtual reality, so that students can quickly grasp the essentials and experience of sports technology, and can accurately see the complete method of technology, deduct points for technology, and make sports technology teaching more comfortable. Finally, in view of the demand for virtual reality technology in sports technology courses in colleges and universities and the problems existing in the integration of virtual reality and sports technology courses, key combing and suggestions are made, in order to enable students to better grasp the technical essence of sports technology.
Low-Cost Home Intrusion Detection System: Attacks and Mitigations

In the last decades, people headed to build smart homes to increase the security surrounding the homes and to prevent unauthorized users from entering secure places without permission based on using biometrics and many sensors. The Ultrasonic Sensor is one of the sensors that has widely been used to detect and measure the distance precisely of any intruder or object by using ultrasonic sound waves, and it’s considered as a low-cost system to build home IDS. By exploiting the home IDS vulnerabilities, the attackers can overcontrol the home and easily enter for different goals such as stealing, damaging their property, violating their privacy using only a single attack. Also, by exploiting the home IDS vulnerabilities the attackers can spread different attacks across the home IDS. Enhancing the security using smart technologies will not be enough, homeowners must use detection and prevention techniques and methods to reduce the effect of the possible attacks and prevent them from occurring. This paper addresses the detection of intruders in homes, the possible attacks on home IDS, and how to mitigate them as well.
Building Machine Learning Bot with ML-Agents in Tank Battle

In recent years, Deep Reinforcement Learning has made great progress in video games, including Atari, ViZDoom, StarCraft, Dota2, and so on. Those successes coupled with the release of the ML-Agents Toolkit, an open-source that helps users to create simulated environments, shows that Deep Reinforcement Learning can now be easily apply to video games. Therefore, stimulating the creativity of developers and researchers. This research aspires to develop a new video game and turn it into a simulation environment for training intelligent agents. Experienced it with tuning the hyperparameters to make the agent getting the best performance for a final commercial video game product.
Sentiment Analysis Online Tools: An Evaluation Study

A sentiment analysis tool interprets text chats and assesses each opinion’s style, purpose, and feeling. The tool can better understand the context of users’ discussions, allowing the client service team to classify client feedback accurately. This is especially valuable for companies that actively address clients’ inquiries and complaints on social media, live chat, and email. Despite its vitality for business, there is still a challenge to decide the sentiment behind the content, especially for the Arabic language. Although most are not available for public usage, many sentiment analysis models and tools are developed in the literature. However, there is a lack of research identifying these tools’ practicality for the Arabic language. This paper investigates two pure online Arabic sentiment analysis tools by employing a sizeable Arabic dataset in the experiments. Prediction quality measurements were utilized to assess these tools. The yielded results recommended Sentest SA as a promised tool for detecting sentiment analysis polarity for the preprocessed Arabic social network contents.
Sentiment Analysis to Extract Public Feelings on Covid-19 Vaccination

Covid-19 (Corona virus) hits the world with wildness, affecting various sectors of life. The whole world has united to confront the virus, and different vaccines were developed to vaccinate the largest possible percentage as an effort to reach community immunity to limit its spread. Governments seek to measure public opinion about vaccination campaigns to improve the quality of services provided. One of the most effective ways to do this is to use artificial intelligence to sense and analyze what the public is posting on social media such as Twitter to ensure that their opinion is known without bias. The study used Twitter API to retrieve Arabic tweets then measured public acceptance of vaccination against Covid-19 disease by using sentiment analysis combined with deep learning as a technique that ensures access to people’s opinions quickly and at a very low cost. The results of this study showed that most people are having a positive opinion on the vaccination with different percentages vary from a vaccine type to another.
Say Aye to AI: Customer Acceptance and Intention to Use Service Robots in the Hospitality Industry

As industrial revolution 4.0 is introduced, many turn into the use of technology and artificial intelligence (AI) in creating a new competitive advantage to business as well as create an automation that ease the operations and increases profitability. With the important contributions of the tourism and hospitality industry, the advantages of using AI can be benefited. Consequently, it is vital for business to understand customers perception towards the use of AI and services robots. Thus, this study aims to investigate the relationship of eight items under three elements of the service robot acceptance model with the acceptance and intention to use service robots. The results of the study show that the perceived usefulness, trust and rapport are significantly related to acceptance of service robots, which in turn, positively related to intention to use them. Implications of the research findings are discussed to support the notion should the industry Say Aye to AI.
Modeling for Performance Evaluation of Quantum Network

Quantum networks are emerging sciences and are anticipated to be the core networking technologies in the future. Due to the difficulty of implementing quantum networks in a real way, because quantum devices are not widely available, they only exist within their laboratories. In addition, they are costly and also need special environments that are not easy to obtain in other than laboratories. In this paper, the authors build a simulator using the language of Python programming to simulate quantum networks in terms of quantum devices, such as repeaters, final nodes and channels, where the behavior of these elements within the network is simulated for the purpose of sending quantum information represented by quantum bits, and therefore the work will be within the principle of the graph and finally facilitate experiments on networks Quantum devices without the need for real physical devices. The most remarkable result that emerged from the simulated data generated and detected is that the modeling process provides guidance for quantum networks design, characterization of their protocols, and their behavior. As a result of this study, one could simulate a quantum network repeater and end node as well as a quantum link (entanglement link) and implement some of the quantum protocols like Quantum Key Distribution (QKD), Teleportation and quantum protocol. In the end, it is concluded the possibility of simulating the behavior of the quantum network, its devices, and protocols, as well as implementing it and developing the quantum applications, an integrated study about the quantum internet and its routing in it. In addition, we were able to develop a quantum repeater protocol in order to enable end-to-end entanglement.
Determinants of Consumers’ Acceptance of Voice Assistance Technology: Integrating the Service Robot Acceptance Model and Unified Theory of Acceptance and Use of Technology

Users are increasingly using AI-based applications in their daily activities, with voice assistants playing a more advanced and pervasive role. However, little is known about the specific drivers of user acceptance of voice assistance technology. Therefore, a conceptual model was developed that integrates factors from the Service Robot Acceptance Model (sRAM), and Unified Theory of Acceptance and Use of Technology (UTAUT). The model will be tested with a large-scale survey of respondents of different ages, genders, and educational levels. This ongoing research focuses on users’ acceptance of the use of digital voice assistants in their daily lives and suggests a model that considers the factors that enable their acceptance.
Bayesian network based vulnerability detection of blockchain smart contracts

Ethereum is one of the popular Blockchain platform. The key component in the Ethereum Blockchain is the smart contract. Smart contracts (SC) are like normal computer programs which are written mostly in solidity high-level object-oriented programming language. Smart contracts allow completing transactions directly between two parties in the network without any middle man or mediator. Modification of the smart contracts are not possible once deployed into the Blockchain. Thus smart contract has to be vulnerable free before deploying into the Blockchain. In this paper, Bayesian Network Model was designed and constructed based on Bayesian learning concept to detect smart contract security vulnerabilities which are Reentrancy, Tx.origin and DOS. The results showed that the proposed BNMC (Bayesian Network Model Construction) design is able to detect the severity of each vulnerability and also suggest the reasons for the vulnerability. The accuracy of the proposed BNMC results are improved (accuracy 8% increased for both Reentracy and Tx.origin, 6% increased for DOS), compared with traditional method LSTM. This proposed BNMS design and implementation is the first attempt to detect smart contract vulnerabilities using Bayesian Networks.
Novel cluster set optimization model with unique identifier tagging for twitter data analysis

Blogs, internet forums, social networks, and micro-blogging sites are some of the growing number of places where users can voice their opinions. Opinions on any given product, issue, service, or idea are contained in data, making them a valuable resource in their own right. Popular social networking services like Twitter, Facebook, and Google+ allows expressing views on a variety of topics, participating in discussions, or sending messages to a global user. Twitter sentiment analysis has received a lot of attention recently.Sentiment analysis is finding how a person feels about a topic from their written response about it and it can be separated into positive and negative through its use. Doing so enables to classify the tweets made by a user in to appropriate classification category based on which some decisions can be made. The literature proposed approaches to develop the classifiers on the Twitter datasets. Operations, including tokenization, stop-word removal, and stemming will be performed. NLP converts the text to a machine-readable representation. Artificial Intelligence (AI) combines NLP data to evaluate if a situation is positive or negative. The document’s subjectivity can be identified using ML and NLP techniques to categorize them in to positive, neutral, or negative. Performing sentiment analysis in Twitter data can be tedious due to limited size, unstructured nature, misspellings, slang, and abbreviations. For this task, a Tweet Analyzing Model for Cluster Set Optimization with Unique Identifier Tagging (TAM-CSO-UIT) was built using prospects to determine positive or negative sentiment in tweets obtained from Twitter. This approach assigns a +ve/-ve value to each entry in the Tweet database based on probability assignment using n-gram model. To perform this effectively the tweet dataset is considered as a sliding window of length L. The proposed model accurately analyses and classifies the tweets.
Prevention of The Use of VPN with Firewall Method Layer 7 Microtic Protocol for Negative Content Filtering and Implementation in RT/RW Net Network (Klatakan Village)

The impact of negative content is still widely published in various media, the influence is in the form of pornographic content. To stay away from such content, it is always necessary to emphasize again the basic principles that must be known for using the internet. One of them is to instill healthy internet usage ethics. Internet control can be carried out in the server mechanism in each regional internet network down to the smallest server unit. The web filtering method using the layer 7 protocol feature is one method to achieve the goal of blocking some negative sites and VPNs. In this research, the implementation of web filtering was carried out on the RT/RW net network in Klatakan Pandaan Village. The network is for the benefit of the client, especially the RT/RW net of Klatakan Pandaan Village.
A privacy protection solution based on data aggregation and batch authentication

The crowdsourcing application improves the data collection capability of the IoV. However, the identity of crowdsourcing participant is public relative to the crowdsourcing server in IoV crowdsourcing. The identity private of vehicle users is easy to leak. Worse, due to the large number of crowdsourcing participants, crowdsourcing data collection will produce relatively large communication loss. In order to solve these problems, this paper proposes a batch authentication-based privacy protection scheme suitable for the IoV crowdsourcing. First, in order to ensure the privacy and availability of data, this paper selects the homomorphic encryption algorithm for encryption processing. Then, by using the properties of homomorphic encryption, the encrypted data is aggregated to effectively reduce the calculation and communication overhead of roadside units. In addition, for the authentication between crowdsourcing participants, this paper adopts bilinear pairing technology to realize batch authentication of identities and reduce the authentication overhead. Finally, it is verified by simulation that the proposed scheme can effectively reduce communication cost while protecting privacy information of vehicle users.
Modular language product lines

Modelling languages are intensively used in paradigms like model-driven engineering to automate all tasks of the development process. These languages may have variants, in which case the need arises to deal with language families rather than with individual languages. However, specifying the syntax and semantics of each language variant separately is costly, hinders reuse across variants, and may yield inconsistent semantics between variants.
Accelerating similarity-based model matching using on-the-fly similarity preserving hashing

Similarity-based model matching is the foundation of model versioning. It pairs model elements based on a distance metric (e.g., edit distance). Because it is expensive to calculate the distance between two elements, a similarity-based matcher usually suffers from performance issues when the model size increases. This paper proposes a hash-based approach to accelerate similarity-based model matching. Firstly, we design a novel similarity-preserving hash function that maps a model element to a 64-bit hash value. If two elements are similar, their hashes are also very close. Secondly, we propose a 3-layer index structure and a query algorithm to quickly filter out impossible candidates for the element to be matched based on their hashes. For the remaining candidates, we employ the classical similarity-based matching algorithm to determine the final matches. Our approach has been realized and integrated into EMF Compare. The evaluation results show that our hash function is effective to preserve the similarity between model elements and our matching approach reduces 16%--72% of time costs while assuring the matching results consistent with EMF Compare.
SetMealAsYouLike

By using semantic segmentation dataset with pixel-wise annotation for training GANs, image generation from a given mask image drawn by a user is possible. However, regarding mask-based food image synthesis, the existing food segmentation datasets have only food region masks and no plate region masks. When we train a mask-based image synthesis network with the datasets without plate mask annotation, the plate regions in the generated food images are uncontrollable by a user and tend to be distorted. To solve this problem, we use a Few-shot segmentation method to estimate the plate regions of the image in the existing food segmentation dataset using a limited number of plate region annotations, and add dish region masks to it. By using added plate masks as training data, we enable generating food images under the control of the shape of the plates. We have implemented the interactive food image drawing system in which we draw food masks as well as plate masks. In the demo, we demonstrate that we generate natural set meal images which include multiple dishes by the sketch interface easily.
Predicate abstractions for smart contract validation

Smart contracts are immutable programs deployed on the blockchain that can manage significant assets. Because of this, verification and validation of smart contracts is of vital importance. Indeed, it is industrial practice to hire independent specialized companies to audit smart contracts before deployment. Auditors typically rely on a combination of tools and experience but still fail to identify problems in smart contracts before deployment, causing significant losses. In this paper, we propose using predicate abstraction to construct models which can be used by auditors to explore and validate smart contact behaviour at the function call level by proposing predicates that expose different aspects of the contract. We propose predicates based on requires clauses and enum-type state variables as a starting point for contract validation and report on an evaluation on two different benchmarks.