Reviewerly/exHarmony huggingface dataset:
Information System for State-owned inventories Management at the Faculty of Computer Science

Information technology development brings a very broad impact on human life. With this Model View Controller (MVC) concept, information system development could be developed in accordance needs of the user’s information system development and be able to adapt the development in the organization. Needs of fast and precise and adaptive information system information is needed in this modern era when everything needs to be fast. Faculty of computer science (FIK) Universitas Pembangunan Nasional Veterans Jakarta (UPNVJ) as one work unit under the government agency, has State-owned inventories to support all activities to carry out the Tri Dharma or the higher education pillars, especially in the field of education. The state-owned inventories or devices are available in FIK such as monitors, printers, LCD, mouse, etc. For continuity of this educational process, the state-owned inventories must be maintained. Therefore, in this research, the researchers will develop an information system to document the state-owned inventory records for helping the management of the state-owned inventories in the faculty of computer science at UPN Veteran Jakarta. The information system was built using the waterfall method and using the PHP programming language and MySQL database running on a server. This information system provides features to manage users, manage components, access system logs, access system settings, manage devices, manage the location, and access reports. The system that has been developed has also passed the eligibility assessment for all aspects of both visual communication aspects, software engineering aspects, and usability aspects.
An image-based approach for building fuzzy color spaces

Color is one of the most used features for image analysis. However, two uncertainty problems arise in this scope: first, color computer representation does not match with how humans understand the color concept; second, the color feature is imprecise by nature. For this reason, fuzzy colors and fuzzy color spaces were developed as a suitable way to model color categories. The most recent and best accurate approaches use crisp color prototypes to build fuzzy color spaces, considering standard and fixed set of colors prototypes (such as ISCC-NBS system) for this purpose. The use of these types of general purpose prototypes may not be flexible enough to collect particular colors of a given image (or a set of images); in addition, image colors may be context dependent in some cases (for example, red color in winery or medicine context). In order to solve these drawbacks, in this paper we propose to learn fuzzy color spaces by analyzing the relevant colors in a given set of representative images for a specific context and/or application; then, we will use them as prototypes in order to build adaptive fuzzy color spaces. Some real experiments are performed in order to illustrate the advantages of our proposal.
Operational effectiveness evaluation based on the reduced conjunctive belief rule base

To address the issue of rule premise combination explosion in the construction of the traditional complete conjunctive belief rule base (BRB), this paper introduces an orthogonal design method to reduce the conjunctive BRB. The reasoning method based on reduced conjunctive BRB is designed with the help of the conversion technology from conjunctive BRB to disjunctive BRB. Finally, the operational mission effectiveness evaluation is taken as an example to verify the proposed method. The results show that the method proposed in this paper is feasible and effective.
Autonomous tracking and landing of an unmanned aerial vehicle on a ground vehicle in rough terrain

Collaborative exploration using unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) has become increasingly popular in academia over the past decade. This study addresses the task of autonomously landing a UAV on a ground vehicle in rough terrain, which is fundamental, yet challenging, for UAV-UGV collaborative exploration. The method proposed in this paper estimates the relative pose and velocity between UAV and UGV and uses model predictive control for UAV trajectory planning while considering the field of view of camera onboard the UAV. Through a simulation study, we confirmed that the proposed method enables a UAV to accurately track the UGV traversing in rough terrain, and to land on it with 100% success rate at the UGV velocity of 1.0 m/s and still 50% at 3.0 m/s.
QuerySnout

Although query-based systems (QBS) have become one of the main solutions to share data anonymously, building QBSes that robustly protect the privacy of individuals contributing to the dataset is a hard problem. Theoretical solutions relying on differential privacy guarantees are difficult to implement correctly with reasonable accuracy, while ad-hoc solutions might contain unknown vulnerabilities. Evaluating the privacy provided by QBSes must thus be done by evaluating the accuracy of a wide range of privacy attacks. However, existing attacks against QBSes require time and expertise to develop, need to be manually tailored to the specific systems attacked, and are limited in scope. In this paper, we develop QuerySnout, the first method to automatically discover vulnerabilities in query-based systems. QuerySnout takes as input a target record and the QBS as a black box, analyzes its behavior on one or more datasets, and outputs a multiset of queries together with a rule to combine answers to them in order to reveal the sensitive attribute of the target record. QuerySnout uses evolutionary search techniques based on a novel mutation operator to find a multiset of queries susceptible to lead to an attack, and a machine learning classifier to infer the sensitive attribute from answers to the queries selected. We showcase the versatility of QuerySnout by applying it to two attack scenarios (assuming access to either the private dataset or to a different dataset from the same distribution), three real-world datasets, and a variety of protection mechanisms. We show the attacks found by QuerySnout to consistently equate or outperform, sometimes by a large margin, the best attacks from the literature. We finally show how QuerySnout can be extended to QBSes that require a budget, and apply QuerySnout to a simple QBS based on the Laplace mechanism. Taken together, our results show how powerful and accurate attacks against QBSes can already be found by an automated system, allowing for highly complex QBSes to be automatically tested "at the pressing of a button". We believe this line of research to be crucial to improve the robustness of systems providing privacy-preserving access to personal data in theory and in practice.
A Multi-Frame GLMB Smoothing Based on the Image-Observation Sensor for Tracking Multiple Weak Targets Using Belief Propagation

The previous multi-frame version of the generalized labeled multi-Bernoulli model (MF-GLMB) only accounts for standard measurement models. It is not suitable for application in the detection and tracking of multiple weak targets (low signal-to-noise ratio) due to the measurement information loss. In this paper, we introduce a MF-GLMB model that formally incorporates a track-before-detect scheme for point targets using an image sensor model. Furthermore, a belief propagation algorithm is adopted to approximately calculate the marginal association probabilities of the multi-target posterior density. In this formulation, an MF-GLMB model based on the track-before-detect measurement model (MF-GLMB-TBD smoothing) enables multi-target posterior recursion for multi-target state estimation. By taking the entire history of the state estimation into account, MF-GLMB-TBD smoothing achieves superior performance in estimation precision compared with the corresponding GLMB-TBD filter. The simulation results demonstrate that the performance of the proposed algorithm is comparable to or better than that of the Gibbs sampler-based version.
Intelligent Trust based e-learning based IDS system and VANET in 6G

The conceptualization of the 6th era of versatile remote organizations (6G) has previously begun for certain potential problematic innovations reverberating as empowering influences for driving the development of various inventive applications. Especially, 6G will be a conspicuous ally for the development of a genuinely Wise Transportation Framework and the acknowledgement of the smart art City idea by satisfying the limits of 5G, when vehicular organizations are turning out to be profoundly unique and complex with rigid necessities on low latency, highly complex and dynamic quality. Vehicular Specially appointed Organizations are a significant Is the set of MANETs. VANET is the gathering of the vehicles which are associated with one an another remotely. A security loss can be major damage for others . Guaranteeing security, (IDS) are carried out in VANETs for distinguishing any interruption. Here, IDS examines the organization and distinguishes any noxious hub if present. In this research paper, we examine the working of VANET with the help of the Intrusion Detection technique and associated with 6G technology.
An Edge-Supported Blockchain-Based Secure Authentication Method and a Cryptocurrency-Based Billing System for P2P Charging of Electric Vehicles

The popularity of electric vehicles (EVs) is constantly increasing, as they use relatively greener, sustainable energy. However, it is a fact that the charging stations for EVs are yet to meet the demand. It could be a great solution if a peer-to-peer (P2P) charging system could be initiated by anyone who wants to make their garage’s charge points publicly available for commercial purposes, named a home charging station (HCS). In this work, our idea is to bring interested charging stations under a network of nodes and a blockchain-based management system, where the blockchain is responsible for ensuring the authenticity of both the charging stations and charge receiver. A cryptocurrency-based payment system has also been proposed to ensure transactions’ security, integrity, transparency, and immutability. A reputation management system is applied to maintain the quality of service. Miners with high processing power are used to alleviate lagging during block creation, supported by edge servers. The proposed system has been implemented by using virtual machines. A theoretical analysis is presented to assess the compatibility and possible cost requirements to implement the system in a real-world scenario.
Progressive Motion Context Refine Network for Efficient Video Frame Interpolation

Recently, flow-based frame interpolation methods have achieved great success by first modeling optical flow between target and input frames, and then building synthesis network for target frame generation. However, above cascaded architecture can lead to large model size and inference delay, hindering them from mobile and real-time applications. To solve this problem, we propose a novel Progressive Motion Context Refine Network (PMCRNet) to predict motion fields and image context jointly for higher efficiency. Different from others that directly synthesize target frame from deep feature, we explore to simplify frame interpolation task by borrowing existing texture from adjacent input frames, which means that decoder in each pyramid level of our PMCRNet only needs to update easier intermediate optical flow, occlusion merge mask and image residual. Moreover, we introduce a new annealed multi-scale reconstruction loss to better guide the learning process of this efficient PMCRNet. Experiments on multiple benchmarks show that proposed approaches not only achieve favorable quantitative and qualitative results but also reduces current model size and running time significantly.
What is behind a facial emoji? The effects of context, age, and gender on children’s understanding of emoji

This study aimed to evaluate the dimensional and semantic meanings of emoji in the absence of context as well as in a food-related context. The effects of age and gender on this perception were also identified in both situations. After a pre-selection task, 312 children (6–12 years) participated in a survey in which the dimensional and semantic meanings of emoji were evaluated in one of two situations. The dimensional meaning was reported as valence and arousal dimensions using a 100-unit visual analogue scale. The semantic meaning was measured by selecting child-friendly emotion terms in a check-all-that-apply question. The results show that the dimensional meaning of emoji was context-dependent. The evocation of a food-related context made emoji more extremely valenced, whereas no specific trend was identified for arousal. Gender and age influenced the dimensional meaning of emoji, but only for individual icons. Girls in the study rated specific neutral and negative emoji more negatively than boys. Younger children (6–8 years) perceived individual active emoji as more arousing than their older peers (9–12 years). Moreover, the semantic meaning of emoji was context-dependent, and specific groups of emoji also differed depending on the age of the participants. In this regard, secondary semantic meanings proved to be influenced by these factors, whereas main semantic meanings were stable. Multivariate analysis indicated that the associations established between emotion word categories and emoji were mainly influenced by valence and the power dimension rather than arousal.
BGSD: A SBERT and GAT-based Service Discovery Framework for Heterogeneous Distributed IoT

Collaboration between heterogeneous IoT (Internet of Things) devices can enrich the capabilities of the system. In scenarios where network infrastructure or cloud platforms are unavailable, enabling devices to collaborate autonomously in a distributed manner can improve the scalability and invulnerability of the system. A distributed service architecture with semantic services can enable more flexible collaboration among IoT devices, while the efficiency and accuracy of current methods on devices with poor computing and storage capacity need to be improved. Therefore, this paper proposes a SBERT (Sentence Bidirectional Encoder Representations from Transformers) and GAT (Graph Attention Networks) based Service Discovery method, called BGSD. SBERT is used to extract deep semantic features of services during offline to improve the discriminability of semantic features and reduce performance requirements for IoT devices, and GAT is used to perceive the local topology environment to improve the network navigability of service search. To evaluate the method, we generate a heterogeneous IoT topology dataset based on OWLS-TC4. The simulation results of service discovery show that BGSD has advantages in search efficiency, matching accuracy and computational cost, and can support the collaboration of IoT devices in heterogeneous distributed scenarios, which verifies the rationality of this method.
Learning With Proper Partial Labels

Abstract Partial-label learning is a kind of weakly supervised learning with inexact labels, where for each training example, we are given a set of candidate labels instead of only one true label. Recently, various approaches on partial-label learning have been proposed under different generation models of candidate label sets. However, these methods require relatively strong distributional assumptions on the generation models. When the assumptions do not hold, the performance of the methods is not guaranteed theoretically. In this letter, we propose the notion of properness on partial labels. We show that this proper partial-label learning framework requires a weaker distributional assumption and includes many previous partial-label learning settings as special cases. We then derive a unified unbiased estimator of the classification risk. We prove that our estimator is risk consistent, and we also establish an estimation error bound. Finally, we validate the effectiveness of our algorithm through experiments.
On the statistics of pairs of logarithms of integers

We study the correlations of pairs of logarithms of positive integers at various scalings, either with trivial weigths or with weights given by the Euler function, proving the existence of pair correlation functions. We prove that at the linear scaling, the pair correlations exhibit level repulsion, as it sometimes occurs in statistical physics. We prove total loss of mass phenomena at superlinear scalings, and Poissonian behaviour at sublinear scalings. The case of Euler weights has applications to the pair correlation of the lengths of common perpendicular geodesic arcs from the maximal Margulis cusp neighborhood to itself in the modular curve $\operatorname{PSL}_2(\mathbb Z)\backslash\mathbb H^2_{\mathbb R}$.
AR Search Engine: Semantic Information Retrieval for Augmented Reality Domain

With the emergence of the metaverse, the popularity of augmented reality (AR) is increasing; accessing concise, accurate, and precise information in this field is becoming challenging on the world wide web. In regard to accessing the right information through search engines, semantic information retrieval via a semantic analysis delivers more relevant information pertaining to the user’s query. However, there is insufficient research on developing semantic information retrieval methods in the AR domain that ranks and clusters AR-based search results in a fair fashion. This paper develops an AR search engine that automatically organizes, understands, searches, and summarizes web documents to enhance the relevancy scores in AR domains. The engine enables users to organize and manage relevant AR documents in various AR concepts and efficiently discover more accurate results in terms of relevancy in the AR field. First, we propose an AR ontology for clustering AR documents into AR topics and concepts. Second, we developed an ontology-based clustering method using the k-means clustering algorithm, vector space model, and term frequency-inverse document frequency (TF-IDF) weighting model with ontology to explore and cluster the AR documents. Third, an experiment was designed to evaluate the proposed AR search engine and compare it with the custom search engine in the AR domains. The results showed that the AR search engine accessed the right information about 42.33% faster and with a 34% better ranking.
Moving Object Detection Based on a Combination of Kalman Filter and Median Filtering

The task of determining the distance from one object to another is one of the important tasks solved in robotics systems. Conventional algorithms rely on an iterative process of predicting distance estimates, which results in an increased computational burden. Algorithms used in robotic systems should require minimal time costs, as well as be resistant to the presence of noise. To solve these problems, the paper proposes an algorithm for Kalman combination filtering with a Goldschmidt divisor and a median filter. Software simulation showed an increase in the accuracy of predicting the estimate of the developed algorithm in comparison with the traditional filtering algorithm, as well as an increase in the speed of the algorithm. The results obtained can be effectively applied in various computer vision systems.
A Hybrid Cryptosystem based on Latin Square and the Modified BB84 Quantum Key Distribution

A new algorithm to improve the security of the transmitted data over the communication channels is presented in this paper. This algorithm is combining Latin square with the modified version of the BB84 Quantum Key Distribution protocol. As the order of the Latin square increases, then , which is the total number of Latin squares of order- , increases quickly. Moreover, the modified BB84 key distribution protocol is a secure method to exchange the encryption keys between two parties. The reason behind that is that the modified BB84 uses the Legendre symbol to generate the quantum key, and it uses the quantum channel only to perform the distribution process instead of using both channels, classic and quantum, as in the standard BB84 protocol. Therefore, the proposed algorithm is secure, reliable and efficient for future communications
Interpretable by Design: Learning Predictors by Composing Interpretable Queries

There is a growing concern about typically opaque decision-making with high-performance machine learning algorithms. Providing an explanation of the reasoning process in domain-specific terms can be crucial for adoption in risk-sensitive domains such as healthcare. We argue that machine learning algorithms should be interpretable by design and that the language in which these interpretations are expressed should be domain- and task-dependent. Consequently, we base our model's prediction on a family of user-defined and task-specific binary functions of the data, each having a clear interpretation to the end-user. We then minimize the expected number of queries needed for accurate prediction on any given input. As the solution is generally intractable, following prior work, we choose the queries sequentially based on information gain. However, in contrast to previous work, we need not assume the queries are conditionally independent. Instead, we leverage a stochastic generative model (VAE) and an MCMC algorithm (Unadjusted Langevin) to select the most informative query about the input based on previous query-answers. This enables the online determination of a query chain of whatever depth is required to resolve prediction ambiguities. Finally, experiments on vision and NLP tasks demonstrate the efficacy of our approach and its superiority over post-hoc explanations.
Survey Instrument Validation for a Learner’s Motivation to Attain Digital Badge for Micro-Credential Courses: Cognitive Interview and Validity Test

COVID-19 has lately changed the way that people learn and teach by making it accessible at any time, from any location, and for a reasonable price. Traditional face-to-face teaching and learning are losing in popularity as more students choose hybrid or online mode study. The gig economy expansion in today's fast-paced business requires the cultivation of people and specialists with increasingly specific skill sets to fill increasingly specialized positions. Micro-Credential is a prominent actor in 21st-century training and education that offers higher education providers a more dynamic and quick learning style to match these demands. It is observed that learners in Malaysia are substantially new to the Micro-Credential and Digital Badge. Hence, this paper aims to present the findings on survey instrument validation in measuring the motivation of learners to attain the digital badge. The study uses a cognitive interviewing method for pre-test in validating the items of the adopted survey instrument. Then, in a pilot test, the survey instrument is again evaluated to measure the validity and reliability of the items using SPSS statistical software. The findings from the pre-test summarized the feedback and suggestions for improvements in fitting the study context and language used. The improved survey instrument undergoes a pilot test to confirm the validity and reliability of the survey items. The finalized survey instrument is then adopted for the main study with a larger sample of learners – university students, working professionals, business owners, and non-working adults using purposive sampling.
KRAKEN

Data marketplaces are online platforms that provide a way for individuals to monetize their (personal) data. In traditional data marketplaces, the data is uploaded to the marketplace platform in plain-text. Since the broker has access to all data, such marketplaces are risk to the users' privacy.